Fusion methods for time-series classification:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Frankfurt, M. ; Berlin ; Bern ; Bruxelles ; New York, NY ; Oxford ; Wien
Lang
2011
|
| Schriftenreihe: | Informationstechnologie und Ökonomie
45 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIII, 144 Seiten graph. Darst. 22 cm |
| ISBN: | 9783631630853 |
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Datensatz im Suchindex
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| adam_text | IMAGE 1
CONTENTS
I PRELIMINARIES 1 INTRODUCTION 3
1.1 OUTLINE 4
2 BACKGROUND 7
2.1 BASIC DEFINITIONS AND PROBLEM FORMULATION 7
2.1.1 VARIANTS OF TIME-SERIES CLASSIFICATION 9
2.1.2 TIME-SERIES CLASSIFICATION AND VECTOR CLASSIFICATION . . .. 10 2.2
TIME SERIES REPRESENTATIONS 10
2.2.1 DISCRETE FOURIER TRANSFORM 11
2.2.2 DISCRETE WAVELET TRANSFORM 12
2.2.3 SYMBOLIC AGGREGATE APPROXIMATION 13
2.3 DISTANCE MEASURES 14
2.3.1 EUCLIDEAN DISTANCE 16
2.3.2 DYNAMIC TIME WARPING 16
2.3.3 EDIT DISTANCE ON REAL SEQUENCES 24
2.3.4 EDIT DISTANCE WITH REAL PENALTY 25
2.3.5 DISTANCE BASED ON LONGEST COMMON SUBSEQUENCES 27 2.3.6 DISSIM 28
2.4 TIME-SERIES CLASSIFICATION ALGORITHMS 29
2.4.1 LOCAL ADAPTATIONS OF THE FC-NN CLASSIFIER 29
2.4.2 SPEEDING-UP TIME-SERIES CLASSIFICATION 31
2.4.3 HUBS IN TIME SERIES DATA 32
2.4.4 CLASSIFICATION BASED ON FEATURE EXTRACTION 35
2.5 FUSION METHODS 38
2.6 EVALUATION OF TIME SERIES CLASSIFIERS 40
2.7 TIME-SERIES CLASSIFICATION TASKS AND BENCHMARK DATA 41 2.7.1
ANALYSIS OF ELECTROCARDIOGRAPH SIGNALS 41
2.7.2 SHAPE RECOGNITION IN IMAGES 45
2.8 SUMMARY 45
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/1016473028
DIGITALISIERT DURCH
IMAGE 2
VIII CONTENTS
II FUSION METHODS FOR TIME-SERIES CLASSIFICATION 47
3 INDIVIDUAL QUALITY ESTIMATION 49
3.1 MOTIVATING EXAMPLE 50
3.2 OUTLINE OF IQ ESTIMATION 51
3.2.1 IQ-MAX 52
3.2.2 IQ-WV 53
3.3 IQ ESTIMATION FOR TIME-SERIES CLASSIFICATION 53
3.3.1 EFFICIENT IMPLEMENTATION 55
3.4 EXPERIMENTAL EVALUATION 56
3.4.1 EXPERIMENTAL RESULTS 58
3.4.2 EXECUTION TIME 59
3.4.3 META MODEL S QUALITY 63
3.5 IQ-REG: IQ ESTIMATION FOR REGRESSION 64
3.5.1 EXPERIMENTS WITH IQ-REG 65
4 INSTANCE SELECTION 67
4.1 SCORE FUNCTIONS BASED ON HUBNESS 68
4.2 COVERAGE AND INSTANCE SELECTION 69
4.2.1 COVERAGE GRAPHS FOR INSTANCE-BASED LEARNING 69
4.2.2 1-NN COVERAGE GRAPHS 72
4.2.3 COVERAGE GRAPH FOR RELATIVE SCORES 73
4.3 EXPERIMENTS 73
4.3.1 RESULTS ON EFFECTIVENESS 74
4.3.2 RESULTS ON EFFICIENCY 77
5 FUSION OF DISTANCE MEASURES 79
5.1 ASPECTS OF SIMILARITY CAPTURED BY VARIOUS TIME SERIES DISTANCE
MEASURES 79
5.2 FUSION OF DISTANCE MEASURES 80
5.3 EXPERIMENTS 82
6 THE GRAMOFON ENSEMBLE FRAMEWORK 85
6.1 CONTEXT AND NOTATIONS 87
6.2 GENERAL MODEL-SELECTION FRAMEWORK 89
6.2.1 MODEL-NETWORKS 89
6.2.2 ENSEMBLE FRAMEWORK 91
6.3 ENSEMBLE TECHNIQUES 92
6.3.1 BASIC 93
6.3.2 BASICFAST 93
6.3.3 REGOPTMST 94
6.3.4 NETWORKMST 94
6.3.5 ANALYSIS 95
IMAGE 3
CONTENTS IX
6.4 EXPERIMENTAL EVALUATION 96
6.4.1 EXPERIMENTS ON THE AUSDM DATASETS 96
6.4.2 EXPERIMENTS ON UCI DATASETS 98
6.5 GRAMOFON FOR TIME-SERIES CLASSIFICATION AND REGRESSION . . .. 100
6.5.1 EXPERIMENTS ON TIME SERIES DATA 100
7 MOTIFS FOR TIME-SERIES CLASSIFICATION 103
7.1 GENERALIZED SEMI-CONTIGUOUS MOTIFS 103
7.1.1 DEFINITIONS 103
7.1.2 ANTI-MONOTONICITY CONSTRAINTS 104
7.1.3 EXTENSIONS OF APRIORI 105
7.1.4 EXTENSIONS OF ECLAT 108
7.2 EXPERIMENTS 112
III CONCLUSIONS AND OUTLOOK 115
8 OUTLOOK: SOME RELATED APPLICATIONS 117
8.1 CLASSIFICATION OF ELECTROCARDIOGRAPH SIGNALS 117
8.2 EXTRACTION OF SEMANTIC RELATIONS 118
8.3 WEB PEOPLE SEARCH 119
8.4 CLUSTERING IMAGES ACCORDING TO EVENTS 120
8.5 ONTOLOGY INDUCTION 120
8.6 ANALYSIS OF AEROPLANE ENGINE VIBRATION 121
9 CONCLUSIONS AND FUTURE WORK 123
9.1 EXTENSIONS OF THE PROPOSED TECHNIQUES 123
9.2 SOME OPEN QUESTIONS 124
9.3 CONCLUSIONS 125
|
| adam_txt |
IMAGE 1
CONTENTS
I PRELIMINARIES 1 INTRODUCTION 3
1.1 OUTLINE 4
2 BACKGROUND 7
2.1 BASIC DEFINITIONS AND PROBLEM FORMULATION 7
2.1.1 VARIANTS OF TIME-SERIES CLASSIFICATION 9
2.1.2 TIME-SERIES CLASSIFICATION AND VECTOR CLASSIFICATION . . . 10 2.2
TIME SERIES REPRESENTATIONS 10
2.2.1 DISCRETE FOURIER TRANSFORM 11
2.2.2 DISCRETE WAVELET TRANSFORM 12
2.2.3 SYMBOLIC AGGREGATE APPROXIMATION 13
2.3 DISTANCE MEASURES 14
2.3.1 EUCLIDEAN DISTANCE 16
2.3.2 DYNAMIC TIME WARPING 16
2.3.3 EDIT DISTANCE ON REAL SEQUENCES 24
2.3.4 EDIT DISTANCE WITH REAL PENALTY 25
2.3.5 DISTANCE BASED ON LONGEST COMMON SUBSEQUENCES 27 2.3.6 DISSIM 28
2.4 TIME-SERIES CLASSIFICATION ALGORITHMS 29
2.4.1 LOCAL ADAPTATIONS OF THE FC-NN CLASSIFIER 29
2.4.2 SPEEDING-UP TIME-SERIES CLASSIFICATION 31
2.4.3 HUBS IN TIME SERIES DATA 32
2.4.4 CLASSIFICATION BASED ON FEATURE EXTRACTION 35
2.5 FUSION METHODS 38
2.6 EVALUATION OF TIME SERIES CLASSIFIERS 40
2.7 TIME-SERIES CLASSIFICATION TASKS AND BENCHMARK DATA 41 2.7.1
ANALYSIS OF ELECTROCARDIOGRAPH SIGNALS 41
2.7.2 SHAPE RECOGNITION IN IMAGES 45
2.8 SUMMARY 45
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/1016473028
DIGITALISIERT DURCH
IMAGE 2
VIII CONTENTS
II FUSION METHODS FOR TIME-SERIES CLASSIFICATION 47
3 INDIVIDUAL QUALITY ESTIMATION 49
3.1 MOTIVATING EXAMPLE 50
3.2 OUTLINE OF IQ ESTIMATION 51
3.2.1 IQ-MAX 52
3.2.2 IQ-WV 53
3.3 IQ ESTIMATION FOR TIME-SERIES CLASSIFICATION 53
3.3.1 EFFICIENT IMPLEMENTATION 55
3.4 EXPERIMENTAL EVALUATION 56
3.4.1 EXPERIMENTAL RESULTS 58
3.4.2 EXECUTION TIME 59
3.4.3 META MODEL'S QUALITY 63
3.5 IQ-REG: IQ ESTIMATION FOR REGRESSION 64
3.5.1 EXPERIMENTS WITH IQ-REG 65
4 INSTANCE SELECTION 67
4.1 SCORE FUNCTIONS BASED ON HUBNESS 68
4.2 COVERAGE AND INSTANCE SELECTION 69
4.2.1 COVERAGE GRAPHS FOR INSTANCE-BASED LEARNING 69
4.2.2 1-NN COVERAGE GRAPHS 72
4.2.3 COVERAGE GRAPH FOR RELATIVE SCORES 73
4.3 EXPERIMENTS 73
4.3.1 RESULTS ON EFFECTIVENESS 74
4.3.2 RESULTS ON EFFICIENCY 77
5 FUSION OF DISTANCE MEASURES 79
5.1 ASPECTS OF SIMILARITY CAPTURED BY VARIOUS TIME SERIES DISTANCE
MEASURES 79
5.2 FUSION OF DISTANCE MEASURES 80
5.3 EXPERIMENTS 82
6 THE GRAMOFON ENSEMBLE FRAMEWORK 85
6.1 CONTEXT AND NOTATIONS 87
6.2 GENERAL MODEL-SELECTION FRAMEWORK 89
6.2.1 MODEL-NETWORKS 89
6.2.2 ENSEMBLE FRAMEWORK 91
6.3 ENSEMBLE TECHNIQUES 92
6.3.1 BASIC 93
6.3.2 BASICFAST 93
6.3.3 REGOPTMST 94
6.3.4 NETWORKMST 94
6.3.5 ANALYSIS 95
IMAGE 3
CONTENTS IX
6.4 EXPERIMENTAL EVALUATION 96
6.4.1 EXPERIMENTS ON THE AUSDM DATASETS 96
6.4.2 EXPERIMENTS ON UCI DATASETS 98
6.5 GRAMOFON FOR TIME-SERIES CLASSIFICATION AND REGRESSION . . . 100
6.5.1 EXPERIMENTS ON TIME SERIES DATA 100
7 MOTIFS FOR TIME-SERIES CLASSIFICATION 103
7.1 GENERALIZED SEMI-CONTIGUOUS MOTIFS 103
7.1.1 DEFINITIONS 103
7.1.2 ANTI-MONOTONICITY CONSTRAINTS 104
7.1.3 EXTENSIONS OF APRIORI 105
7.1.4 EXTENSIONS OF ECLAT 108
7.2 EXPERIMENTS 112
III CONCLUSIONS AND OUTLOOK 115
8 OUTLOOK: SOME RELATED APPLICATIONS 117
8.1 CLASSIFICATION OF ELECTROCARDIOGRAPH SIGNALS 117
8.2 EXTRACTION OF SEMANTIC RELATIONS 118
8.3 WEB PEOPLE SEARCH 119
8.4 CLUSTERING IMAGES ACCORDING TO EVENTS 120
8.5 ONTOLOGY INDUCTION 120
8.6 ANALYSIS OF AEROPLANE ENGINE VIBRATION 121
9 CONCLUSIONS AND FUTURE WORK 123
9.1 EXTENSIONS OF THE PROPOSED TECHNIQUES 123
9.2 SOME OPEN QUESTIONS 124
9.3 CONCLUSIONS 125 |
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| id | DE-604.BV047087757 |
| illustrated | Illustrated |
| index_date | 2024-07-03T16:18:23Z |
| indexdate | 2024-07-10T09:02:13Z |
| institution | BVB |
| isbn | 9783631630853 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032494407 |
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| physical | XIII, 144 Seiten graph. Darst. 22 cm |
| publishDate | 2011 |
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| publisher | Lang |
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| series | Informationstechnologie und Ökonomie |
| series2 | Informationstechnologie und Ökonomie |
| spelling | Buza, Krisztian Antal Verfasser aut Fusion methods for time-series classification Krisztian Antal Buza Frankfurt, M. ; Berlin ; Bern ; Bruxelles ; New York, NY ; Oxford ; Wien Lang 2011 XIII, 144 Seiten graph. Darst. 22 cm txt rdacontent n rdamedia nc rdacarrier Informationstechnologie und Ökonomie 45 Automatische Klassifikation (DE-588)4120957-6 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 s Automatische Klassifikation (DE-588)4120957-6 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Informationstechnologie und Ökonomie 45 (DE-604)BV014032758 45 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032494407&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Buza, Krisztian Antal Fusion methods for time-series classification Informationstechnologie und Ökonomie Automatische Klassifikation (DE-588)4120957-6 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4120957-6 (DE-588)4067486-1 (DE-588)4193754-5 |
| title | Fusion methods for time-series classification |
| title_auth | Fusion methods for time-series classification |
| title_exact_search | Fusion methods for time-series classification |
| title_exact_search_txtP | Fusion methods for time-series classification |
| title_full | Fusion methods for time-series classification Krisztian Antal Buza |
| title_fullStr | Fusion methods for time-series classification Krisztian Antal Buza |
| title_full_unstemmed | Fusion methods for time-series classification Krisztian Antal Buza |
| title_short | Fusion methods for time-series classification |
| title_sort | fusion methods for time series classification |
| topic | Automatische Klassifikation (DE-588)4120957-6 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Automatische Klassifikation Zeitreihenanalyse Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032494407&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV014032758 |
| work_keys_str_mv | AT buzakrisztianantal fusionmethodsfortimeseriesclassification |