Machine Learning, Optimization, and Data Science: 8th International Conference, LOD 2022, Certosa Di Pontignano, Italy, September 18-22, 2022, Revised Selected Papers, Part II.
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| Format: | Electronic eBook |
| Language: | English |
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Cham
Springer International Publishing AG
2023
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| Edition: | 1st ed |
| Series: | Lecture Notes in Computer Science Series
v.13811 |
| Subjects: | |
| Online Access: | DE-2070s |
| Item Description: | Description based on publisher supplied metadata and other sources |
| Physical Description: | 1 Online-Ressource (605 Seiten) |
| ISBN: | 9783031258916 |
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| 505 | 8 | |a Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Pooling Graph Convolutional Networks for Structural Performance Prediction -- 1 Background -- 2 Related Work -- 3 Multi-objective Residual Pooling Graph Convolutional Networks -- 4 Datasets -- 4.1 Pre-processing -- 5 Experiments -- 5.1 Evaluation Methodology -- 5.2 Performance Prediction on NAS-Bench-101 -- 5.3 Performance Prediction on Evofficient -- 5.4 Performance Prediction on NAS-Bench-201 -- 5.5 Ablation Studies -- 5.6 Implementation Details -- 6 Conclusion -- References -- A Game Theoretic Flavoured Decision Tree for Classification -- 1 Introduction -- 2 A Game Theoretic Splitting Mechanism for Decision Trees -- 3 Computational Aspects -- 4 Numerical Experiments -- 5 Conclusions -- References -- Human-Centric Machine Learning Approach for Injection Mold Design: Towards Automated Ejector Pin Placement -- 1 Introduction -- 2 Related Work -- 3 Human-Centric Machine Learning Framework -- 4 Example: Box Geometry -- 5 Conclusion -- References -- A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview -- 3.2 Input Formatting and Encoding -- 3.3 Dataset -- 3.4 Network Architecture -- 3.5 Machine Learning Models Used for Comparison -- 4 Results Discussion and Evaluation -- 4.1 Overall Performance -- 4.2 Evaluation -- 5 Conclusion and Future Work -- References -- Multi-omic Data Integration and Feature Selection for Survival-Based Patient Stratification via Supervised Concrete Autoencoders -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Datasets -- 3.2 Evaluation and Metrics -- 3.3 Main Testing Pipeline -- 3.4 Cox-Supervised Autoencoder -- 3.5 Concrete Supervised Autoencoder -- 3.6 Baselines -- 4 Results -- 5 Discussion -- References | |
| 505 | 8 | |a Smart Pruning of Deep Neural Networks Using Curve Fitting and Evolution of Weights -- 1 Introduction -- 2 Related Work -- 3 Re-thinking Pruning -- 3.1 Evolution of Weights -- 3.2 Smart Pruning -- 4 Proposed Approach -- 4.1 Pruning Distributions -- 4.2 Smart Pruning -- 4.3 Formulation -- 5 Experiment -- 5.1 Settings -- 6 Results -- 6.1 Test on Noisy Data -- 7 Discussion -- References -- Biologically Inspired Unified Artificial Immune System for Industrial Equipment Diagnostic -- 1 Introduction -- 2 Statement of the Problem -- 3 Approaches and Methods -- 3.1 Failure Mode and Effects Analysis -- 3.2 Unified Artificial Immune System -- 4 Development of Equipment Diagnostics System Based on FMEA and UAIS -- 5 Simulation and Experiment Results -- 6 Conclusion -- References -- A Nonparametric Pooling Operator Capable of Texture Extraction -- 1 Introduction -- 2 Texture Encoding -- 3 Total Rank Order for Image Texture Encoding -- 3.1 Explicit Resolution -- 3.2 Order Disagreement Distance -- 4 Rank-Order Principal Components -- 4.1 Importance Ranking -- 4.2 Experiment: Application of Rank-Order Pooling (RO) Principal Components to Textured Image -- 5 Rank-Order Pooling Operator -- 5.1 Pooling Layer -- 5.2 Experiment: RO Pooling for Automatic Tumor Segmentation -- 6 Results and Conclusion -- References -- Strategic Workforce Planning with Deep Reinforcement Learning -- 1 Introduction -- 1.1 Related Work -- 2 Strategic Workforce Planning as Optimization -- 2.1 Cohort Model -- 2.2 Optimizing the Cohort Model -- 3 Simulation-Optimization with Deep Reinforcement Learning -- 3.1 Deep Reinforcement Learning for Workforce Planning -- 3.2 Simulating the Workforce -- 4 Experimental Setup -- 4.1 Baseline -- 4.2 Training Setup -- 4.3 Hypothetical Organization -- 4.4 Real-Life Use Case -- 5 Results -- 6 Discussion -- References -- Enhanced Association Rules and Python | |
| 505 | 8 | |a 1 Introduction -- 2 Association Rules and 4ft-Miner Procedure -- 2.1 Data Matrix and Boolean Attributes -- 2.2 Data Set Adult -- 2.3 GUHA Association Rules -- 2.4 Definition of a Set of Relevant Rules -- 3 CleverMiner Implementation of 4ft-Miner -- 3.1 General Features -- 3.2 Segments with High Relative Frequency of Rich Persons -- 3.3 Segments with High Relative Frequency of Really Rich Persons -- 3.4 Comparing CleverMiner and Apriori -- 4 Iterative Process with 4ft-Miner -- 4.1 Iterative Algorithm -- 5 Conlusions and Further Work -- References -- Generating Vascular Networks: A Reinforcement Learning Approach -- 1 Introduction -- 2 Existing Models for Sprouting Angiogenesis -- 3 Reinforcement Learning -- 3.1 Q-Learning Preliminaries -- 4 Generating Vascular Networks -- 4.1 Model Architecture -- 4.2 Evaluating Vascular Networks -- 5 Experiments -- 6 Conclusion and Future Work -- 6.1 Biological Properties -- 6.2 Reinforcement Learning Methodology -- 6.3 Experimental Design -- References -- Expressive and Intuitive Models for Automated Context Representation Learning in Credit-Card Fraud Detection -- 1 Introduction -- 2 Background -- 3 Context Representation for Fraud Detection -- 3.1 Feature Engineering for Fraud Detection -- 3.2 Neural Aggregate Generator -- 4 Extensions -- 4.1 Shared Functions Across Constraints -- 4.2 Selection of Constraints -- 4.3 Extending Modelling Capabilities of the NAG -- 5 Experiments -- 5.1 Dataset -- 5.2 Pre-processing -- 5.3 Dataset Splits and Alignment -- 5.4 Model Evaluation -- 6 Results and Discussion -- 6.1 Approximation of Manual Aggregates -- 6.2 Fraud Classification Task -- 6.3 Parameter Budget Study -- 7 Conclusion -- References -- Digital Twins: Modelling Languages Comparison -- 1 Introduction -- 2 Digital Twin Background -- 2.1 Digital Twin Concept -- 2.2 Digital Twin in Industry -- 3 DTs Adoption Challenges | |
| 505 | 8 | |a 4 Digital Twin Modelling Languages -- 5 Conclusions -- References -- Algorithms that Get Old: The Case of Generative Deep Neural Networks -- 1 Motivation -- 1.1 Relation to Previous Literature -- 1.2 Technical Goal of the Paper -- 2 Theoretical Results -- 3 Algorithm and Numerical Results -- 3.1 Algorithm Formulation -- 3.2 Numerical Results -- 4 Final Remarks -- References -- A Two-Country Study of Default Risk Prediction Using Bayesian Machine-Learning -- 1 Introduction -- 2 The Model -- 3 Empirical Analysis -- 4 Conclusions -- References -- Enforcing Hard State-Dependent Action Bounds on Deep Reinforcement Learning Policies -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions and Related Work -- 1.3 Organization -- 2 Reinforcement Learning -- 2.1 Deterministic Methods -- 2.2 Stochastic Methods -- 3 Proposed Methodology -- 3.1 Normalized Actions -- 3.2 State-Dependent Rescaling -- 3.3 Preservation of MDP -- 3.4 Implementation -- 4 Experimental Results -- 4.1 Highway Driving -- 5 Conclusion -- A Rescaling Functions -- B State-Dependent Bounds in Gym Environments -- B.1 Inverted Pendulum -- B.2 Lunar Lander -- B.3 SAC Hyperparameters -- C Autonomous Highway Driving Environment -- C.1 Roads -- C.2 Vehicles -- C.3 Policies -- C.4 Reward -- C.5 Action Bounds -- References -- A Bee Colony Optimization Approach for the Electric Vehicle Routing Problem with Drones -- 1 Introduction -- 2 Related Literature -- 2.1 Electric Ground Vehicle Routing Problems -- 2.2 Routing Problems with Drones -- 3 Mathematical Formulation of the EVRPD -- 4 Bee Colony Optimization Approach -- 4.1 Solution Construction -- 4.2 Waggle Dance Mechanism -- 4.3 Local Search -- 5 Experimental Results -- 6 Conclusions -- References -- Best Practices in Flux Sampling of Constrained-Based Models -- 1 Introduction -- 2 Material and Methods -- 2.1 Metabolic Network Model | |
| 505 | 8 | |a 2.2 Sampling Algorithms -- 2.3 Convergence Diagnostics -- 2.4 Statistical Analysis -- 3 Experimental Setting -- 4 Results -- 4.1 CHRR Mitigates the Risk of False-Discoveries -- 4.2 Filtering on Fold-Change Reduces the Risk of False Discoveries -- 4.3 Standard Diagnostic Analysis Does Not Prevent False-Discoveries -- 4.4 Sample Size Increases Similarity but Does Not Decrease FDR -- 5 Conclusions -- References -- EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python -- 1 Introduction -- 2 EFI Toolbox -- 2.1 An Overview of EFI Framework -- 2.2 Toolbox Modules -- 3 Case Study -- 3.1 The Iris Dataset -- 3.2 Data Pre-processing -- 3.3 Model Optimisation and Training -- 3.4 Feature Importance Coefficients -- 3.5 Model Specific Ensemble Feature Importance -- 3.6 Multi-method Ensemble Feature Importance -- 3.7 Fuzzy Ensemble Feature Importance -- 4 Conclusion -- References -- Hierarchical Decentralized Deep Reinforcement Learning Architecture for a Simulated Four-Legged Agent -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Experimental Setup -- 3.2 Tasks -- 4 Results -- 4.1 First Task: Navigate to a Specific Goal -- 4.2 Second Task: Seek Target -- 4.3 Third Task: Seek Target in Maze -- 5 Conclusion -- References -- Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time Series -- 1 Introduction -- 2 Background and Problem Formulation -- 2.1 Robust Principal Component Analysis -- 2.2 Robust Principal Component Analysis with Temporal Regularisations -- 2.3 Online Robust Principal Component Analysis with Temporal Regularisations -- 3 Method -- 3.1 Batch Temporal Algorithm -- 3.2 Online Temporal Algorithm via Stochastic Optimisation -- 3.3 Hyperparameters Search -- 4 Experiments -- 4.1 Synthetic Data -- 4.2 Real Data: Ticketing Validations Time Series -- 5 Discussion and Conclusion -- References | |
| 505 | 8 | |a Monte Carlo Optimization of Liver Machine Perfusion Temperature Policies | |
| 650 | 4 | |a Machine learning-Congresses | |
| 650 | 4 | |a Mathematical optimization-Congresses | |
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| contents | Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Pooling Graph Convolutional Networks for Structural Performance Prediction -- 1 Background -- 2 Related Work -- 3 Multi-objective Residual Pooling Graph Convolutional Networks -- 4 Datasets -- 4.1 Pre-processing -- 5 Experiments -- 5.1 Evaluation Methodology -- 5.2 Performance Prediction on NAS-Bench-101 -- 5.3 Performance Prediction on Evofficient -- 5.4 Performance Prediction on NAS-Bench-201 -- 5.5 Ablation Studies -- 5.6 Implementation Details -- 6 Conclusion -- References -- A Game Theoretic Flavoured Decision Tree for Classification -- 1 Introduction -- 2 A Game Theoretic Splitting Mechanism for Decision Trees -- 3 Computational Aspects -- 4 Numerical Experiments -- 5 Conclusions -- References -- Human-Centric Machine Learning Approach for Injection Mold Design: Towards Automated Ejector Pin Placement -- 1 Introduction -- 2 Related Work -- 3 Human-Centric Machine Learning Framework -- 4 Example: Box Geometry -- 5 Conclusion -- References -- A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview -- 3.2 Input Formatting and Encoding -- 3.3 Dataset -- 3.4 Network Architecture -- 3.5 Machine Learning Models Used for Comparison -- 4 Results Discussion and Evaluation -- 4.1 Overall Performance -- 4.2 Evaluation -- 5 Conclusion and Future Work -- References -- Multi-omic Data Integration and Feature Selection for Survival-Based Patient Stratification via Supervised Concrete Autoencoders -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Datasets -- 3.2 Evaluation and Metrics -- 3.3 Main Testing Pipeline -- 3.4 Cox-Supervised Autoencoder -- 3.5 Concrete Supervised Autoencoder -- 3.6 Baselines -- 4 Results -- 5 Discussion -- References Smart Pruning of Deep Neural Networks Using Curve Fitting and Evolution of Weights -- 1 Introduction -- 2 Related Work -- 3 Re-thinking Pruning -- 3.1 Evolution of Weights -- 3.2 Smart Pruning -- 4 Proposed Approach -- 4.1 Pruning Distributions -- 4.2 Smart Pruning -- 4.3 Formulation -- 5 Experiment -- 5.1 Settings -- 6 Results -- 6.1 Test on Noisy Data -- 7 Discussion -- References -- Biologically Inspired Unified Artificial Immune System for Industrial Equipment Diagnostic -- 1 Introduction -- 2 Statement of the Problem -- 3 Approaches and Methods -- 3.1 Failure Mode and Effects Analysis -- 3.2 Unified Artificial Immune System -- 4 Development of Equipment Diagnostics System Based on FMEA and UAIS -- 5 Simulation and Experiment Results -- 6 Conclusion -- References -- A Nonparametric Pooling Operator Capable of Texture Extraction -- 1 Introduction -- 2 Texture Encoding -- 3 Total Rank Order for Image Texture Encoding -- 3.1 Explicit Resolution -- 3.2 Order Disagreement Distance -- 4 Rank-Order Principal Components -- 4.1 Importance Ranking -- 4.2 Experiment: Application of Rank-Order Pooling (RO) Principal Components to Textured Image -- 5 Rank-Order Pooling Operator -- 5.1 Pooling Layer -- 5.2 Experiment: RO Pooling for Automatic Tumor Segmentation -- 6 Results and Conclusion -- References -- Strategic Workforce Planning with Deep Reinforcement Learning -- 1 Introduction -- 1.1 Related Work -- 2 Strategic Workforce Planning as Optimization -- 2.1 Cohort Model -- 2.2 Optimizing the Cohort Model -- 3 Simulation-Optimization with Deep Reinforcement Learning -- 3.1 Deep Reinforcement Learning for Workforce Planning -- 3.2 Simulating the Workforce -- 4 Experimental Setup -- 4.1 Baseline -- 4.2 Training Setup -- 4.3 Hypothetical Organization -- 4.4 Real-Life Use Case -- 5 Results -- 6 Discussion -- References -- Enhanced Association Rules and Python 1 Introduction -- 2 Association Rules and 4ft-Miner Procedure -- 2.1 Data Matrix and Boolean Attributes -- 2.2 Data Set Adult -- 2.3 GUHA Association Rules -- 2.4 Definition of a Set of Relevant Rules -- 3 CleverMiner Implementation of 4ft-Miner -- 3.1 General Features -- 3.2 Segments with High Relative Frequency of Rich Persons -- 3.3 Segments with High Relative Frequency of Really Rich Persons -- 3.4 Comparing CleverMiner and Apriori -- 4 Iterative Process with 4ft-Miner -- 4.1 Iterative Algorithm -- 5 Conlusions and Further Work -- References -- Generating Vascular Networks: A Reinforcement Learning Approach -- 1 Introduction -- 2 Existing Models for Sprouting Angiogenesis -- 3 Reinforcement Learning -- 3.1 Q-Learning Preliminaries -- 4 Generating Vascular Networks -- 4.1 Model Architecture -- 4.2 Evaluating Vascular Networks -- 5 Experiments -- 6 Conclusion and Future Work -- 6.1 Biological Properties -- 6.2 Reinforcement Learning Methodology -- 6.3 Experimental Design -- References -- Expressive and Intuitive Models for Automated Context Representation Learning in Credit-Card Fraud Detection -- 1 Introduction -- 2 Background -- 3 Context Representation for Fraud Detection -- 3.1 Feature Engineering for Fraud Detection -- 3.2 Neural Aggregate Generator -- 4 Extensions -- 4.1 Shared Functions Across Constraints -- 4.2 Selection of Constraints -- 4.3 Extending Modelling Capabilities of the NAG -- 5 Experiments -- 5.1 Dataset -- 5.2 Pre-processing -- 5.3 Dataset Splits and Alignment -- 5.4 Model Evaluation -- 6 Results and Discussion -- 6.1 Approximation of Manual Aggregates -- 6.2 Fraud Classification Task -- 6.3 Parameter Budget Study -- 7 Conclusion -- References -- Digital Twins: Modelling Languages Comparison -- 1 Introduction -- 2 Digital Twin Background -- 2.1 Digital Twin Concept -- 2.2 Digital Twin in Industry -- 3 DTs Adoption Challenges 4 Digital Twin Modelling Languages -- 5 Conclusions -- References -- Algorithms that Get Old: The Case of Generative Deep Neural Networks -- 1 Motivation -- 1.1 Relation to Previous Literature -- 1.2 Technical Goal of the Paper -- 2 Theoretical Results -- 3 Algorithm and Numerical Results -- 3.1 Algorithm Formulation -- 3.2 Numerical Results -- 4 Final Remarks -- References -- A Two-Country Study of Default Risk Prediction Using Bayesian Machine-Learning -- 1 Introduction -- 2 The Model -- 3 Empirical Analysis -- 4 Conclusions -- References -- Enforcing Hard State-Dependent Action Bounds on Deep Reinforcement Learning Policies -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions and Related Work -- 1.3 Organization -- 2 Reinforcement Learning -- 2.1 Deterministic Methods -- 2.2 Stochastic Methods -- 3 Proposed Methodology -- 3.1 Normalized Actions -- 3.2 State-Dependent Rescaling -- 3.3 Preservation of MDP -- 3.4 Implementation -- 4 Experimental Results -- 4.1 Highway Driving -- 5 Conclusion -- A Rescaling Functions -- B State-Dependent Bounds in Gym Environments -- B.1 Inverted Pendulum -- B.2 Lunar Lander -- B.3 SAC Hyperparameters -- C Autonomous Highway Driving Environment -- C.1 Roads -- C.2 Vehicles -- C.3 Policies -- C.4 Reward -- C.5 Action Bounds -- References -- A Bee Colony Optimization Approach for the Electric Vehicle Routing Problem with Drones -- 1 Introduction -- 2 Related Literature -- 2.1 Electric Ground Vehicle Routing Problems -- 2.2 Routing Problems with Drones -- 3 Mathematical Formulation of the EVRPD -- 4 Bee Colony Optimization Approach -- 4.1 Solution Construction -- 4.2 Waggle Dance Mechanism -- 4.3 Local Search -- 5 Experimental Results -- 6 Conclusions -- References -- Best Practices in Flux Sampling of Constrained-Based Models -- 1 Introduction -- 2 Material and Methods -- 2.1 Metabolic Network Model 2.2 Sampling Algorithms -- 2.3 Convergence Diagnostics -- 2.4 Statistical Analysis -- 3 Experimental Setting -- 4 Results -- 4.1 CHRR Mitigates the Risk of False-Discoveries -- 4.2 Filtering on Fold-Change Reduces the Risk of False Discoveries -- 4.3 Standard Diagnostic Analysis Does Not Prevent False-Discoveries -- 4.4 Sample Size Increases Similarity but Does Not Decrease FDR -- 5 Conclusions -- References -- EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python -- 1 Introduction -- 2 EFI Toolbox -- 2.1 An Overview of EFI Framework -- 2.2 Toolbox Modules -- 3 Case Study -- 3.1 The Iris Dataset -- 3.2 Data Pre-processing -- 3.3 Model Optimisation and Training -- 3.4 Feature Importance Coefficients -- 3.5 Model Specific Ensemble Feature Importance -- 3.6 Multi-method Ensemble Feature Importance -- 3.7 Fuzzy Ensemble Feature Importance -- 4 Conclusion -- References -- Hierarchical Decentralized Deep Reinforcement Learning Architecture for a Simulated Four-Legged Agent -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Experimental Setup -- 3.2 Tasks -- 4 Results -- 4.1 First Task: Navigate to a Specific Goal -- 4.2 Second Task: Seek Target -- 4.3 Third Task: Seek Target in Maze -- 5 Conclusion -- References -- Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time Series -- 1 Introduction -- 2 Background and Problem Formulation -- 2.1 Robust Principal Component Analysis -- 2.2 Robust Principal Component Analysis with Temporal Regularisations -- 2.3 Online Robust Principal Component Analysis with Temporal Regularisations -- 3 Method -- 3.1 Batch Temporal Algorithm -- 3.2 Online Temporal Algorithm via Stochastic Optimisation -- 3.3 Hyperparameters Search -- 4 Experiments -- 4.1 Synthetic Data -- 4.2 Real Data: Ticketing Validations Time Series -- 5 Discussion and Conclusion -- References Monte Carlo Optimization of Liver Machine Perfusion Temperature Policies |
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| discipline | Allgemeines |
| edition | 1st ed |
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Mold Design: Towards Automated Ejector Pin Placement -- 1 Introduction -- 2 Related Work -- 3 Human-Centric Machine Learning Framework -- 4 Example: Box Geometry -- 5 Conclusion -- References -- A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview -- 3.2 Input Formatting and Encoding -- 3.3 Dataset -- 3.4 Network Architecture -- 3.5 Machine Learning Models Used for Comparison -- 4 Results Discussion and Evaluation -- 4.1 Overall Performance -- 4.2 Evaluation -- 5 Conclusion and Future Work -- References -- Multi-omic Data Integration and Feature Selection for Survival-Based Patient Stratification via Supervised Concrete Autoencoders -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Datasets -- 3.2 Evaluation and Metrics -- 3.3 Main Testing Pipeline -- 3.4 Cox-Supervised Autoencoder -- 3.5 Concrete Supervised Autoencoder -- 3.6 Baselines -- 4 Results -- 5 Discussion -- References</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Smart Pruning of Deep Neural Networks Using Curve Fitting and Evolution of Weights -- 1 Introduction -- 2 Related Work -- 3 Re-thinking Pruning -- 3.1 Evolution of Weights -- 3.2 Smart Pruning -- 4 Proposed Approach -- 4.1 Pruning Distributions -- 4.2 Smart Pruning -- 4.3 Formulation -- 5 Experiment -- 5.1 Settings -- 6 Results -- 6.1 Test on Noisy Data -- 7 Discussion -- References -- Biologically Inspired Unified Artificial Immune System for Industrial Equipment Diagnostic -- 1 Introduction -- 2 Statement of the Problem -- 3 Approaches and Methods -- 3.1 Failure Mode and Effects Analysis -- 3.2 Unified Artificial Immune System -- 4 Development of Equipment Diagnostics System Based on FMEA and UAIS -- 5 Simulation and Experiment Results -- 6 Conclusion -- References -- A Nonparametric Pooling Operator Capable of Texture Extraction -- 1 Introduction -- 2 Texture Encoding -- 3 Total Rank Order for Image Texture Encoding -- 3.1 Explicit Resolution -- 3.2 Order Disagreement Distance -- 4 Rank-Order Principal Components -- 4.1 Importance Ranking -- 4.2 Experiment: Application of Rank-Order Pooling (RO) Principal Components to Textured Image -- 5 Rank-Order Pooling Operator -- 5.1 Pooling Layer -- 5.2 Experiment: RO Pooling for Automatic Tumor Segmentation -- 6 Results and Conclusion -- References -- Strategic Workforce Planning with Deep Reinforcement Learning -- 1 Introduction -- 1.1 Related Work -- 2 Strategic Workforce Planning as Optimization -- 2.1 Cohort Model -- 2.2 Optimizing the Cohort Model -- 3 Simulation-Optimization with Deep Reinforcement Learning -- 3.1 Deep Reinforcement Learning for Workforce Planning -- 3.2 Simulating the Workforce -- 4 Experimental Setup -- 4.1 Baseline -- 4.2 Training Setup -- 4.3 Hypothetical Organization -- 4.4 Real-Life Use Case -- 5 Results -- 6 Discussion -- References -- Enhanced Association Rules and Python</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">1 Introduction -- 2 Association Rules and 4ft-Miner Procedure -- 2.1 Data Matrix and Boolean Attributes -- 2.2 Data Set Adult -- 2.3 GUHA Association Rules -- 2.4 Definition of a Set of Relevant Rules -- 3 CleverMiner Implementation of 4ft-Miner -- 3.1 General Features -- 3.2 Segments with High Relative Frequency of Rich Persons -- 3.3 Segments with High Relative Frequency of Really Rich Persons -- 3.4 Comparing CleverMiner and Apriori -- 4 Iterative Process with 4ft-Miner -- 4.1 Iterative Algorithm -- 5 Conlusions and Further Work -- References -- Generating Vascular Networks: A Reinforcement Learning Approach -- 1 Introduction -- 2 Existing Models for Sprouting Angiogenesis -- 3 Reinforcement Learning -- 3.1 Q-Learning Preliminaries -- 4 Generating Vascular Networks -- 4.1 Model Architecture -- 4.2 Evaluating Vascular Networks -- 5 Experiments -- 6 Conclusion and Future Work -- 6.1 Biological Properties -- 6.2 Reinforcement Learning Methodology -- 6.3 Experimental Design -- References -- Expressive and Intuitive Models for Automated Context Representation Learning in Credit-Card Fraud Detection -- 1 Introduction -- 2 Background -- 3 Context Representation for Fraud Detection -- 3.1 Feature Engineering for Fraud Detection -- 3.2 Neural Aggregate Generator -- 4 Extensions -- 4.1 Shared Functions Across Constraints -- 4.2 Selection of Constraints -- 4.3 Extending Modelling Capabilities of the NAG -- 5 Experiments -- 5.1 Dataset -- 5.2 Pre-processing -- 5.3 Dataset Splits and Alignment -- 5.4 Model Evaluation -- 6 Results and Discussion -- 6.1 Approximation of Manual Aggregates -- 6.2 Fraud Classification Task -- 6.3 Parameter Budget Study -- 7 Conclusion -- References -- Digital Twins: Modelling Languages Comparison -- 1 Introduction -- 2 Digital Twin Background -- 2.1 Digital Twin Concept -- 2.2 Digital Twin in Industry -- 3 DTs Adoption Challenges</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4 Digital Twin Modelling Languages -- 5 Conclusions -- References -- Algorithms that Get Old: The Case of Generative Deep Neural Networks -- 1 Motivation -- 1.1 Relation to Previous Literature -- 1.2 Technical Goal of the Paper -- 2 Theoretical Results -- 3 Algorithm and Numerical Results -- 3.1 Algorithm Formulation -- 3.2 Numerical Results -- 4 Final Remarks -- References -- A Two-Country Study of Default Risk Prediction Using Bayesian Machine-Learning -- 1 Introduction -- 2 The Model -- 3 Empirical Analysis -- 4 Conclusions -- References -- Enforcing Hard State-Dependent Action Bounds on Deep Reinforcement Learning Policies -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions and Related Work -- 1.3 Organization -- 2 Reinforcement Learning -- 2.1 Deterministic Methods -- 2.2 Stochastic Methods -- 3 Proposed Methodology -- 3.1 Normalized Actions -- 3.2 State-Dependent Rescaling -- 3.3 Preservation of MDP -- 3.4 Implementation -- 4 Experimental Results -- 4.1 Highway Driving -- 5 Conclusion -- A Rescaling Functions -- B State-Dependent Bounds in Gym Environments -- B.1 Inverted Pendulum -- B.2 Lunar Lander -- B.3 SAC Hyperparameters -- C Autonomous Highway Driving Environment -- C.1 Roads -- C.2 Vehicles -- C.3 Policies -- C.4 Reward -- C.5 Action Bounds -- References -- A Bee Colony Optimization Approach for the Electric Vehicle Routing Problem with Drones -- 1 Introduction -- 2 Related Literature -- 2.1 Electric Ground Vehicle Routing Problems -- 2.2 Routing Problems with Drones -- 3 Mathematical Formulation of the EVRPD -- 4 Bee Colony Optimization Approach -- 4.1 Solution Construction -- 4.2 Waggle Dance Mechanism -- 4.3 Local Search -- 5 Experimental Results -- 6 Conclusions -- References -- Best Practices in Flux Sampling of Constrained-Based Models -- 1 Introduction -- 2 Material and Methods -- 2.1 Metabolic Network Model</subfield></datafield><datafield 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Learning Architecture for a Simulated Four-Legged Agent -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Experimental Setup -- 3.2 Tasks -- 4 Results -- 4.1 First Task: Navigate to a Specific Goal -- 4.2 Second Task: Seek Target -- 4.3 Third Task: Seek Target in Maze -- 5 Conclusion -- References -- Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time Series -- 1 Introduction -- 2 Background and Problem Formulation -- 2.1 Robust Principal Component Analysis -- 2.2 Robust Principal Component Analysis with Temporal Regularisations -- 2.3 Online Robust Principal Component Analysis with Temporal Regularisations -- 3 Method -- 3.1 Batch Temporal Algorithm -- 3.2 Online Temporal Algorithm via Stochastic Optimisation -- 3.3 Hyperparameters Search -- 4 Experiments -- 4.1 Synthetic Data -- 4.2 Real Data: Ticketing Validations Time Series -- 5 Discussion and Conclusion -- References</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Monte 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| genre | (DE-588)1071861417 Konferenzschrift gnd-content |
| genre_facet | Konferenzschrift |
| id | DE-604.BV049872922 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-10T19:04:04Z |
| institution | BVB |
| isbn | 9783031258916 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035212380 |
| oclc_num | 1373984173 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (605 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Springer International Publishing AG |
| record_format | marc |
| series2 | Lecture Notes in Computer Science Series |
| spelling | Nicosia, Giuseppe Verfasser aut Machine Learning, Optimization, and Data Science 8th International Conference, LOD 2022, Certosa Di Pontignano, Italy, September 18-22, 2022, Revised Selected Papers, Part II. 1st ed Cham Springer International Publishing AG 2023 ©2023 1 Online-Ressource (605 Seiten) txt rdacontent c rdamedia cr rdacarrier Lecture Notes in Computer Science Series v.13811 Description based on publisher supplied metadata and other sources Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Pooling Graph Convolutional Networks for Structural Performance Prediction -- 1 Background -- 2 Related Work -- 3 Multi-objective Residual Pooling Graph Convolutional Networks -- 4 Datasets -- 4.1 Pre-processing -- 5 Experiments -- 5.1 Evaluation Methodology -- 5.2 Performance Prediction on NAS-Bench-101 -- 5.3 Performance Prediction on Evofficient -- 5.4 Performance Prediction on NAS-Bench-201 -- 5.5 Ablation Studies -- 5.6 Implementation Details -- 6 Conclusion -- References -- A Game Theoretic Flavoured Decision Tree for Classification -- 1 Introduction -- 2 A Game Theoretic Splitting Mechanism for Decision Trees -- 3 Computational Aspects -- 4 Numerical Experiments -- 5 Conclusions -- References -- Human-Centric Machine Learning Approach for Injection Mold Design: Towards Automated Ejector Pin Placement -- 1 Introduction -- 2 Related Work -- 3 Human-Centric Machine Learning Framework -- 4 Example: Box Geometry -- 5 Conclusion -- References -- A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview -- 3.2 Input Formatting and Encoding -- 3.3 Dataset -- 3.4 Network Architecture -- 3.5 Machine Learning Models Used for Comparison -- 4 Results Discussion and Evaluation -- 4.1 Overall Performance -- 4.2 Evaluation -- 5 Conclusion and Future Work -- References -- Multi-omic Data Integration and Feature Selection for Survival-Based Patient Stratification via Supervised Concrete Autoencoders -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Datasets -- 3.2 Evaluation and Metrics -- 3.3 Main Testing Pipeline -- 3.4 Cox-Supervised Autoencoder -- 3.5 Concrete Supervised Autoencoder -- 3.6 Baselines -- 4 Results -- 5 Discussion -- References Smart Pruning of Deep Neural Networks Using Curve Fitting and Evolution of Weights -- 1 Introduction -- 2 Related Work -- 3 Re-thinking Pruning -- 3.1 Evolution of Weights -- 3.2 Smart Pruning -- 4 Proposed Approach -- 4.1 Pruning Distributions -- 4.2 Smart Pruning -- 4.3 Formulation -- 5 Experiment -- 5.1 Settings -- 6 Results -- 6.1 Test on Noisy Data -- 7 Discussion -- References -- Biologically Inspired Unified Artificial Immune System for Industrial Equipment Diagnostic -- 1 Introduction -- 2 Statement of the Problem -- 3 Approaches and Methods -- 3.1 Failure Mode and Effects Analysis -- 3.2 Unified Artificial Immune System -- 4 Development of Equipment Diagnostics System Based on FMEA and UAIS -- 5 Simulation and Experiment Results -- 6 Conclusion -- References -- A Nonparametric Pooling Operator Capable of Texture Extraction -- 1 Introduction -- 2 Texture Encoding -- 3 Total Rank Order for Image Texture Encoding -- 3.1 Explicit Resolution -- 3.2 Order Disagreement Distance -- 4 Rank-Order Principal Components -- 4.1 Importance Ranking -- 4.2 Experiment: Application of Rank-Order Pooling (RO) Principal Components to Textured Image -- 5 Rank-Order Pooling Operator -- 5.1 Pooling Layer -- 5.2 Experiment: RO Pooling for Automatic Tumor Segmentation -- 6 Results and Conclusion -- References -- Strategic Workforce Planning with Deep Reinforcement Learning -- 1 Introduction -- 1.1 Related Work -- 2 Strategic Workforce Planning as Optimization -- 2.1 Cohort Model -- 2.2 Optimizing the Cohort Model -- 3 Simulation-Optimization with Deep Reinforcement Learning -- 3.1 Deep Reinforcement Learning for Workforce Planning -- 3.2 Simulating the Workforce -- 4 Experimental Setup -- 4.1 Baseline -- 4.2 Training Setup -- 4.3 Hypothetical Organization -- 4.4 Real-Life Use Case -- 5 Results -- 6 Discussion -- References -- Enhanced Association Rules and Python 1 Introduction -- 2 Association Rules and 4ft-Miner Procedure -- 2.1 Data Matrix and Boolean Attributes -- 2.2 Data Set Adult -- 2.3 GUHA Association Rules -- 2.4 Definition of a Set of Relevant Rules -- 3 CleverMiner Implementation of 4ft-Miner -- 3.1 General Features -- 3.2 Segments with High Relative Frequency of Rich Persons -- 3.3 Segments with High Relative Frequency of Really Rich Persons -- 3.4 Comparing CleverMiner and Apriori -- 4 Iterative Process with 4ft-Miner -- 4.1 Iterative Algorithm -- 5 Conlusions and Further Work -- References -- Generating Vascular Networks: A Reinforcement Learning Approach -- 1 Introduction -- 2 Existing Models for Sprouting Angiogenesis -- 3 Reinforcement Learning -- 3.1 Q-Learning Preliminaries -- 4 Generating Vascular Networks -- 4.1 Model Architecture -- 4.2 Evaluating Vascular Networks -- 5 Experiments -- 6 Conclusion and Future Work -- 6.1 Biological Properties -- 6.2 Reinforcement Learning Methodology -- 6.3 Experimental Design -- References -- Expressive and Intuitive Models for Automated Context Representation Learning in Credit-Card Fraud Detection -- 1 Introduction -- 2 Background -- 3 Context Representation for Fraud Detection -- 3.1 Feature Engineering for Fraud Detection -- 3.2 Neural Aggregate Generator -- 4 Extensions -- 4.1 Shared Functions Across Constraints -- 4.2 Selection of Constraints -- 4.3 Extending Modelling Capabilities of the NAG -- 5 Experiments -- 5.1 Dataset -- 5.2 Pre-processing -- 5.3 Dataset Splits and Alignment -- 5.4 Model Evaluation -- 6 Results and Discussion -- 6.1 Approximation of Manual Aggregates -- 6.2 Fraud Classification Task -- 6.3 Parameter Budget Study -- 7 Conclusion -- References -- Digital Twins: Modelling Languages Comparison -- 1 Introduction -- 2 Digital Twin Background -- 2.1 Digital Twin Concept -- 2.2 Digital Twin in Industry -- 3 DTs Adoption Challenges 4 Digital Twin Modelling Languages -- 5 Conclusions -- References -- Algorithms that Get Old: The Case of Generative Deep Neural Networks -- 1 Motivation -- 1.1 Relation to Previous Literature -- 1.2 Technical Goal of the Paper -- 2 Theoretical Results -- 3 Algorithm and Numerical Results -- 3.1 Algorithm Formulation -- 3.2 Numerical Results -- 4 Final Remarks -- References -- A Two-Country Study of Default Risk Prediction Using Bayesian Machine-Learning -- 1 Introduction -- 2 The Model -- 3 Empirical Analysis -- 4 Conclusions -- References -- Enforcing Hard State-Dependent Action Bounds on Deep Reinforcement Learning Policies -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions and Related Work -- 1.3 Organization -- 2 Reinforcement Learning -- 2.1 Deterministic Methods -- 2.2 Stochastic Methods -- 3 Proposed Methodology -- 3.1 Normalized Actions -- 3.2 State-Dependent Rescaling -- 3.3 Preservation of MDP -- 3.4 Implementation -- 4 Experimental Results -- 4.1 Highway Driving -- 5 Conclusion -- A Rescaling Functions -- B State-Dependent Bounds in Gym Environments -- B.1 Inverted Pendulum -- B.2 Lunar Lander -- B.3 SAC Hyperparameters -- C Autonomous Highway Driving Environment -- C.1 Roads -- C.2 Vehicles -- C.3 Policies -- C.4 Reward -- C.5 Action Bounds -- References -- A Bee Colony Optimization Approach for the Electric Vehicle Routing Problem with Drones -- 1 Introduction -- 2 Related Literature -- 2.1 Electric Ground Vehicle Routing Problems -- 2.2 Routing Problems with Drones -- 3 Mathematical Formulation of the EVRPD -- 4 Bee Colony Optimization Approach -- 4.1 Solution Construction -- 4.2 Waggle Dance Mechanism -- 4.3 Local Search -- 5 Experimental Results -- 6 Conclusions -- References -- Best Practices in Flux Sampling of Constrained-Based Models -- 1 Introduction -- 2 Material and Methods -- 2.1 Metabolic Network Model 2.2 Sampling Algorithms -- 2.3 Convergence Diagnostics -- 2.4 Statistical Analysis -- 3 Experimental Setting -- 4 Results -- 4.1 CHRR Mitigates the Risk of False-Discoveries -- 4.2 Filtering on Fold-Change Reduces the Risk of False Discoveries -- 4.3 Standard Diagnostic Analysis Does Not Prevent False-Discoveries -- 4.4 Sample Size Increases Similarity but Does Not Decrease FDR -- 5 Conclusions -- References -- EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python -- 1 Introduction -- 2 EFI Toolbox -- 2.1 An Overview of EFI Framework -- 2.2 Toolbox Modules -- 3 Case Study -- 3.1 The Iris Dataset -- 3.2 Data Pre-processing -- 3.3 Model Optimisation and Training -- 3.4 Feature Importance Coefficients -- 3.5 Model Specific Ensemble Feature Importance -- 3.6 Multi-method Ensemble Feature Importance -- 3.7 Fuzzy Ensemble Feature Importance -- 4 Conclusion -- References -- Hierarchical Decentralized Deep Reinforcement Learning Architecture for a Simulated Four-Legged Agent -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Experimental Setup -- 3.2 Tasks -- 4 Results -- 4.1 First Task: Navigate to a Specific Goal -- 4.2 Second Task: Seek Target -- 4.3 Third Task: Seek Target in Maze -- 5 Conclusion -- References -- Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time Series -- 1 Introduction -- 2 Background and Problem Formulation -- 2.1 Robust Principal Component Analysis -- 2.2 Robust Principal Component Analysis with Temporal Regularisations -- 2.3 Online Robust Principal Component Analysis with Temporal Regularisations -- 3 Method -- 3.1 Batch Temporal Algorithm -- 3.2 Online Temporal Algorithm via Stochastic Optimisation -- 3.3 Hyperparameters Search -- 4 Experiments -- 4.1 Synthetic Data -- 4.2 Real Data: Ticketing Validations Time Series -- 5 Discussion and Conclusion -- References Monte Carlo Optimization of Liver Machine Perfusion Temperature Policies Machine learning-Congresses Mathematical optimization-Congresses Big Data (DE-588)4802620-7 gnd rswk-swf Informatik (DE-588)4026894-9 gnd rswk-swf Optimierung (DE-588)4043664-0 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf (DE-588)1071861417 Konferenzschrift gnd-content Informatik (DE-588)4026894-9 s Maschinelles Lernen (DE-588)4193754-5 s Optimierung (DE-588)4043664-0 s Big Data (DE-588)4802620-7 s DE-604 Ojha, Varun Sonstige oth La Malfa, Emanuele Sonstige oth La Malfa, Gabriele Sonstige oth Pardalos, Panos Sonstige oth Di Fatta, Giuseppe Sonstige oth Giuffrida, Giovanni Sonstige oth Umeton, Renato Sonstige oth Erscheint auch als Druck-Ausgabe Nicosia, Giuseppe Machine Learning, Optimization, and Data Science Cham : Springer International Publishing AG,c2023 9783031258909 |
| spellingShingle | Nicosia, Giuseppe Machine Learning, Optimization, and Data Science 8th International Conference, LOD 2022, Certosa Di Pontignano, Italy, September 18-22, 2022, Revised Selected Papers, Part II. Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Pooling Graph Convolutional Networks for Structural Performance Prediction -- 1 Background -- 2 Related Work -- 3 Multi-objective Residual Pooling Graph Convolutional Networks -- 4 Datasets -- 4.1 Pre-processing -- 5 Experiments -- 5.1 Evaluation Methodology -- 5.2 Performance Prediction on NAS-Bench-101 -- 5.3 Performance Prediction on Evofficient -- 5.4 Performance Prediction on NAS-Bench-201 -- 5.5 Ablation Studies -- 5.6 Implementation Details -- 6 Conclusion -- References -- A Game Theoretic Flavoured Decision Tree for Classification -- 1 Introduction -- 2 A Game Theoretic Splitting Mechanism for Decision Trees -- 3 Computational Aspects -- 4 Numerical Experiments -- 5 Conclusions -- References -- Human-Centric Machine Learning Approach for Injection Mold Design: Towards Automated Ejector Pin Placement -- 1 Introduction -- 2 Related Work -- 3 Human-Centric Machine Learning Framework -- 4 Example: Box Geometry -- 5 Conclusion -- References -- A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview -- 3.2 Input Formatting and Encoding -- 3.3 Dataset -- 3.4 Network Architecture -- 3.5 Machine Learning Models Used for Comparison -- 4 Results Discussion and Evaluation -- 4.1 Overall Performance -- 4.2 Evaluation -- 5 Conclusion and Future Work -- References -- Multi-omic Data Integration and Feature Selection for Survival-Based Patient Stratification via Supervised Concrete Autoencoders -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Datasets -- 3.2 Evaluation and Metrics -- 3.3 Main Testing Pipeline -- 3.4 Cox-Supervised Autoencoder -- 3.5 Concrete Supervised Autoencoder -- 3.6 Baselines -- 4 Results -- 5 Discussion -- References Smart Pruning of Deep Neural Networks Using Curve Fitting and Evolution of Weights -- 1 Introduction -- 2 Related Work -- 3 Re-thinking Pruning -- 3.1 Evolution of Weights -- 3.2 Smart Pruning -- 4 Proposed Approach -- 4.1 Pruning Distributions -- 4.2 Smart Pruning -- 4.3 Formulation -- 5 Experiment -- 5.1 Settings -- 6 Results -- 6.1 Test on Noisy Data -- 7 Discussion -- References -- Biologically Inspired Unified Artificial Immune System for Industrial Equipment Diagnostic -- 1 Introduction -- 2 Statement of the Problem -- 3 Approaches and Methods -- 3.1 Failure Mode and Effects Analysis -- 3.2 Unified Artificial Immune System -- 4 Development of Equipment Diagnostics System Based on FMEA and UAIS -- 5 Simulation and Experiment Results -- 6 Conclusion -- References -- A Nonparametric Pooling Operator Capable of Texture Extraction -- 1 Introduction -- 2 Texture Encoding -- 3 Total Rank Order for Image Texture Encoding -- 3.1 Explicit Resolution -- 3.2 Order Disagreement Distance -- 4 Rank-Order Principal Components -- 4.1 Importance Ranking -- 4.2 Experiment: Application of Rank-Order Pooling (RO) Principal Components to Textured Image -- 5 Rank-Order Pooling Operator -- 5.1 Pooling Layer -- 5.2 Experiment: RO Pooling for Automatic Tumor Segmentation -- 6 Results and Conclusion -- References -- Strategic Workforce Planning with Deep Reinforcement Learning -- 1 Introduction -- 1.1 Related Work -- 2 Strategic Workforce Planning as Optimization -- 2.1 Cohort Model -- 2.2 Optimizing the Cohort Model -- 3 Simulation-Optimization with Deep Reinforcement Learning -- 3.1 Deep Reinforcement Learning for Workforce Planning -- 3.2 Simulating the Workforce -- 4 Experimental Setup -- 4.1 Baseline -- 4.2 Training Setup -- 4.3 Hypothetical Organization -- 4.4 Real-Life Use Case -- 5 Results -- 6 Discussion -- References -- Enhanced Association Rules and Python 1 Introduction -- 2 Association Rules and 4ft-Miner Procedure -- 2.1 Data Matrix and Boolean Attributes -- 2.2 Data Set Adult -- 2.3 GUHA Association Rules -- 2.4 Definition of a Set of Relevant Rules -- 3 CleverMiner Implementation of 4ft-Miner -- 3.1 General Features -- 3.2 Segments with High Relative Frequency of Rich Persons -- 3.3 Segments with High Relative Frequency of Really Rich Persons -- 3.4 Comparing CleverMiner and Apriori -- 4 Iterative Process with 4ft-Miner -- 4.1 Iterative Algorithm -- 5 Conlusions and Further Work -- References -- Generating Vascular Networks: A Reinforcement Learning Approach -- 1 Introduction -- 2 Existing Models for Sprouting Angiogenesis -- 3 Reinforcement Learning -- 3.1 Q-Learning Preliminaries -- 4 Generating Vascular Networks -- 4.1 Model Architecture -- 4.2 Evaluating Vascular Networks -- 5 Experiments -- 6 Conclusion and Future Work -- 6.1 Biological Properties -- 6.2 Reinforcement Learning Methodology -- 6.3 Experimental Design -- References -- Expressive and Intuitive Models for Automated Context Representation Learning in Credit-Card Fraud Detection -- 1 Introduction -- 2 Background -- 3 Context Representation for Fraud Detection -- 3.1 Feature Engineering for Fraud Detection -- 3.2 Neural Aggregate Generator -- 4 Extensions -- 4.1 Shared Functions Across Constraints -- 4.2 Selection of Constraints -- 4.3 Extending Modelling Capabilities of the NAG -- 5 Experiments -- 5.1 Dataset -- 5.2 Pre-processing -- 5.3 Dataset Splits and Alignment -- 5.4 Model Evaluation -- 6 Results and Discussion -- 6.1 Approximation of Manual Aggregates -- 6.2 Fraud Classification Task -- 6.3 Parameter Budget Study -- 7 Conclusion -- References -- Digital Twins: Modelling Languages Comparison -- 1 Introduction -- 2 Digital Twin Background -- 2.1 Digital Twin Concept -- 2.2 Digital Twin in Industry -- 3 DTs Adoption Challenges 4 Digital Twin Modelling Languages -- 5 Conclusions -- References -- Algorithms that Get Old: The Case of Generative Deep Neural Networks -- 1 Motivation -- 1.1 Relation to Previous Literature -- 1.2 Technical Goal of the Paper -- 2 Theoretical Results -- 3 Algorithm and Numerical Results -- 3.1 Algorithm Formulation -- 3.2 Numerical Results -- 4 Final Remarks -- References -- A Two-Country Study of Default Risk Prediction Using Bayesian Machine-Learning -- 1 Introduction -- 2 The Model -- 3 Empirical Analysis -- 4 Conclusions -- References -- Enforcing Hard State-Dependent Action Bounds on Deep Reinforcement Learning Policies -- 1 Introduction -- 1.1 Motivation -- 1.2 Main Contributions and Related Work -- 1.3 Organization -- 2 Reinforcement Learning -- 2.1 Deterministic Methods -- 2.2 Stochastic Methods -- 3 Proposed Methodology -- 3.1 Normalized Actions -- 3.2 State-Dependent Rescaling -- 3.3 Preservation of MDP -- 3.4 Implementation -- 4 Experimental Results -- 4.1 Highway Driving -- 5 Conclusion -- A Rescaling Functions -- B State-Dependent Bounds in Gym Environments -- B.1 Inverted Pendulum -- B.2 Lunar Lander -- B.3 SAC Hyperparameters -- C Autonomous Highway Driving Environment -- C.1 Roads -- C.2 Vehicles -- C.3 Policies -- C.4 Reward -- C.5 Action Bounds -- References -- A Bee Colony Optimization Approach for the Electric Vehicle Routing Problem with Drones -- 1 Introduction -- 2 Related Literature -- 2.1 Electric Ground Vehicle Routing Problems -- 2.2 Routing Problems with Drones -- 3 Mathematical Formulation of the EVRPD -- 4 Bee Colony Optimization Approach -- 4.1 Solution Construction -- 4.2 Waggle Dance Mechanism -- 4.3 Local Search -- 5 Experimental Results -- 6 Conclusions -- References -- Best Practices in Flux Sampling of Constrained-Based Models -- 1 Introduction -- 2 Material and Methods -- 2.1 Metabolic Network Model 2.2 Sampling Algorithms -- 2.3 Convergence Diagnostics -- 2.4 Statistical Analysis -- 3 Experimental Setting -- 4 Results -- 4.1 CHRR Mitigates the Risk of False-Discoveries -- 4.2 Filtering on Fold-Change Reduces the Risk of False Discoveries -- 4.3 Standard Diagnostic Analysis Does Not Prevent False-Discoveries -- 4.4 Sample Size Increases Similarity but Does Not Decrease FDR -- 5 Conclusions -- References -- EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python -- 1 Introduction -- 2 EFI Toolbox -- 2.1 An Overview of EFI Framework -- 2.2 Toolbox Modules -- 3 Case Study -- 3.1 The Iris Dataset -- 3.2 Data Pre-processing -- 3.3 Model Optimisation and Training -- 3.4 Feature Importance Coefficients -- 3.5 Model Specific Ensemble Feature Importance -- 3.6 Multi-method Ensemble Feature Importance -- 3.7 Fuzzy Ensemble Feature Importance -- 4 Conclusion -- References -- Hierarchical Decentralized Deep Reinforcement Learning Architecture for a Simulated Four-Legged Agent -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Experimental Setup -- 3.2 Tasks -- 4 Results -- 4.1 First Task: Navigate to a Specific Goal -- 4.2 Second Task: Seek Target -- 4.3 Third Task: Seek Target in Maze -- 5 Conclusion -- References -- Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time Series -- 1 Introduction -- 2 Background and Problem Formulation -- 2.1 Robust Principal Component Analysis -- 2.2 Robust Principal Component Analysis with Temporal Regularisations -- 2.3 Online Robust Principal Component Analysis with Temporal Regularisations -- 3 Method -- 3.1 Batch Temporal Algorithm -- 3.2 Online Temporal Algorithm via Stochastic Optimisation -- 3.3 Hyperparameters Search -- 4 Experiments -- 4.1 Synthetic Data -- 4.2 Real Data: Ticketing Validations Time Series -- 5 Discussion and Conclusion -- References Monte Carlo Optimization of Liver Machine Perfusion Temperature Policies Machine learning-Congresses Mathematical optimization-Congresses Big Data (DE-588)4802620-7 gnd Informatik (DE-588)4026894-9 gnd Optimierung (DE-588)4043664-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4802620-7 (DE-588)4026894-9 (DE-588)4043664-0 (DE-588)4193754-5 (DE-588)1071861417 |
| title | Machine Learning, Optimization, and Data Science 8th International Conference, LOD 2022, Certosa Di Pontignano, Italy, September 18-22, 2022, Revised Selected Papers, Part II. |
| title_auth | Machine Learning, Optimization, and Data Science 8th International Conference, LOD 2022, Certosa Di Pontignano, Italy, September 18-22, 2022, Revised Selected Papers, Part II. |
| title_exact_search | Machine Learning, Optimization, and Data Science 8th International Conference, LOD 2022, Certosa Di Pontignano, Italy, September 18-22, 2022, Revised Selected Papers, Part II. |
| title_full | Machine Learning, Optimization, and Data Science 8th International Conference, LOD 2022, Certosa Di Pontignano, Italy, September 18-22, 2022, Revised Selected Papers, Part II. |
| title_fullStr | Machine Learning, Optimization, and Data Science 8th International Conference, LOD 2022, Certosa Di Pontignano, Italy, September 18-22, 2022, Revised Selected Papers, Part II. |
| title_full_unstemmed | Machine Learning, Optimization, and Data Science 8th International Conference, LOD 2022, Certosa Di Pontignano, Italy, September 18-22, 2022, Revised Selected Papers, Part II. |
| title_short | Machine Learning, Optimization, and Data Science |
| title_sort | machine learning optimization and data science 8th international conference lod 2022 certosa di pontignano italy september 18 22 2022 revised selected papers part ii |
| title_sub | 8th International Conference, LOD 2022, Certosa Di Pontignano, Italy, September 18-22, 2022, Revised Selected Papers, Part II. |
| topic | Machine learning-Congresses Mathematical optimization-Congresses Big Data (DE-588)4802620-7 gnd Informatik (DE-588)4026894-9 gnd Optimierung (DE-588)4043664-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Machine learning-Congresses Mathematical optimization-Congresses Big Data Informatik Optimierung Maschinelles Lernen Konferenzschrift |
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