Rough sets and intelligent systems: Professor Zdzisław Pawlak in memoriam 2
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Berlin [u.a.]
Springer
(2013)
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| Series: | Intelligent systems reference library
43 |
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| Physical Description: | LI, 604 S. Ill., graf. Darst. |
| ISBN: | 9783642303401 |
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| 245 | 1 | 0 | |a Rough sets and intelligent systems |b Professor Zdzisław Pawlak in memoriam |n 2 |c Andrzej Skowron and Zbigniew Suraj (ed.) |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c (2013) | |
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| 490 | 1 | |a Intelligent systems reference library |v 43 | |
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IMAGE 1
CONTENTS
1 FROM LOGIC TO COMPUTER SCIENCE - A PERSONAL EXPERIENCE 1
ANITA WASILEWSKA REFERENCES 5
2 KNOWLEDGE ALGEBRAS AND THEIR DISCRETE DUALITY 7
EWA ORLOWSKA, ANNA MARIA RADZIKOWSKA 2.1 INTRODUCTION 7
2.2 ROUGH-SET-STYLE INFORMATION OPERATORS 8
2.3 KNOWLEDGE OPERATOR 10
2.4 DISCRETE DUALITY FOR BOOLEAN ALGEBRAS 13
2.5 KNOWLEDGE ALGEBRAS AND KNOWLEDGE FRAMES 14
2.6 REPRESENTATION THEOREMS FOR KNOWLEDGE ALGEBRAS AND KNOWLEDGE FRAMES
17
2.7 CONCLUSIONS 18
REFERENCES 18
3 COMPARISON O F GREEDY ALGORITHMS FOR DECISION TREE OPTIMIZATION . . 21
ABDULAZIZ ALKHALID, IGOR CHIKALOV, MIKHAIL MOSHKOV 3.1 INTRODUCTION 21
3.2 BASIC NOTIONS 22
3.2.1 DECISION TABLES AND TREES 22
3.2.2 UNCERTAINTY MEASURES 2 4
3.2.3 IMPURITY FUNCTIONS 2 4
3.2.4 COST FUNCTIONS 2 4
3.3 GREEDY APPROACH 25
3.4 DYNAMIC PROGRAMMING APPROACH 27
3.5 EXPERIMENTS WITH EXACT DECISION TREES AND DECISION TABLES FROM UCI M
L REPOSITORY 29
3.6 EXPERIMENTS WITH APPROXIMATE DECISION TREES AND DECISION TABLES FROM
UCI M L REPOSITORY 33
HTTP://D-NB.INFO/1021740667
IMAGE 2
XL
CONTENTS
3.7 EXPERIMENTS WITH EXACT DECISION TREES AND RANDOMLY GENERATED
DECISION TABLES 35
3.8 ANALYSIS O F EXPERIMENTAL RESULTS 35
3.9 CONCLUSIONS 38
REFERENCES 38
4 A REVIEW O F THE KNOWLEDGE GRANULATION METHODS: DISCRETE VS.
CONTINUOUS ALGORITHMS 41
PIOTR ARTIEMJEW 4.1 INTRODUCTION 41
4.1.1 BASIC ON ROUGH SETS 4 2
4.1.2 FROM ROUGH INCLUSIONS TO GRANULAR STRUCTURES 4 3
4.2 GENERAL STRATEGIES OF KNOWLEDGE GRANULATION 4 6
4.2.1 THE DECISION SYSTEMS IN PROFESSOR ZDZISLAW PAWLAK'S SENSE 4 6
4.3 APPROXIMATION O F DECISION SYSTEMS METHODS 47
4.3.1 STANDARD GRANULATION 4 7
4.3.2 CONCEPT DEPENDENT GRANULATION 4 9
4.3.3 LAYERED GRANULATION 5 0
4.3.4 CONCEPT DEPENDENT LAYERED GRANULATION 51
4.3.5 -GRANULATION 51
4.3.6 CONCEPT DEPENDENT GRANULATION 53
4.4 EXEMPLARY G - CLASSIFICATION 53
4.4.1 EXEMPLARY RESULTS FOR COMBINATION OF - GRANULATION AND
CLASSIFICATION 54
4.5 CONCLUSIONS 5 6
REFERENCES 57
5 GAME-THEORETIC ROUGH SETS FOR FEATURE SELECTION 61
NOUMAN AZAM, JINGTAO YAO 5.1 INTRODUCTION 61
5.2 GAME-THEORETIC ROUGH SETS 63
5.3 FEATURE SELECTION WITH GAME-THEORETIC ROUGH SET 65
5.3.1 COMPONENTS 65
5.3.2 IMPLEMENTING COMPETITION 68
5.4 A DEMONSTRATIVE EXAMPLE 70
5.5 CONCLUSION 75
REFERENCES 7 6
6 A CLUSTERING APPROACH TO IMAGE RETRIEVAL USING RANGE BASED QUERY AND
MAHALANOBIS DISTANCE 79
MINAKSHI BANERJEE, SANGHAMITRA BANDYOPADHYAY, SANKAR K. PAL 6.1
INTRODUCTION 80
6.2 SYSTEM OVERVIEW 82
6.3 THEORETICAL PRELIMINARIES 82
6.3.1 MAHALANOBIS DISTANCE 82
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CONTENTS XLI
6.3.2 K-MEANS ALGORITHM 83
6.3.3 FEATURE EXTRACTION 84
6.3.4 RANGE BASED QUERY AND MAHALANOBIS DISTANCE 85
6.4 EXPERIMENTAL RESULTS 86
6.5 CONCLUSION 89
REFERENCES 89
7 CLASSIFIERS BASED ON DATA SETS AND DOMAIN KNOWLEDGE: A ROUGH SET
APPROACH 93
JAN G. BAZAN, STANISLAWA BAZAN-SOCHA, SYLWIA BUREGWA-CZUMA, PRZEMYSTAW
WIKTOR PARDEL, ANDRZEJ SKOWRON, BARBARA SOKOTOWSKA 7.1 INTRODUCTION 94
7.2 METHODS O F APPROXIMATION O F SPATIAL CONCEPTS 99
7.2.1 EXPERIMENTS WITH DATA 104
7.3 METHODS O F APPROXIMATION O F SPATIO-TEMPORAL CONCEPTS 106
7.4 METHODS OF BEHAVIORAL PATTERN IDENTIFICATION 108
7.4.1 RISK PATTERN IDENTIFICATION IN MEDICAL DATA 109
7.4.2 MEDICAL TEMPORAL PATTERNS 109
7.4.3 MEDICAL RISK PATTERN 110
7.4.4 EXPERIMENTS WITH MEDICAL DATA I L L
7.5 METHODS O F AUTOMATED PLANNING 115
7.5.1 AUTOMATED PLANNING FOR STRUCTURED COMPLEX OBJECTS . . . 118 7.5.2
ESTIMATION O F THE SIMILARITY BETWEEN PLANS 122
7.5.3 ONTOLOGY O F THE SIMILARITY BETWEEN PLANS 124
7.5.4 SIMILARITY CLASSIFIER 126
7.5.5 EXPERIMENTS WITH MEDICAL DATA 128
7.6 CONCLUSION 132
REFERENCES 133
8 INCORPORATING ROUGH DATA IN DATABASE DESIGN FOR IMPRECISE INFORMATION
REPRESENTATION 137
THERESA BEAUBOUEF, FREDERICK E. PETRY 8.1 INTRODUCTION 137
8.2 ROUGH RELATIONAL DATABASES 139
8.3 E-R MODELING FOR ROUGH DATABASES 141
8.4 ROUGH FUNCTIONAL DEPENDENCIES AND NORMALIZATION 142
8.5 ROUGH NORMAL FORMS 144
8.5.1 , ROUGH SECOND NORMAL FORM 145
8.5.2 ROUGH THIRD NORMAL FORM 145
8.5.3 ROUGH BOYCE CODD NORMAL FORM (BCNF) 147
8.6 SECURITY DESIGN ISSUES 147
8.6.1 SECURITY APPROACHES 147
8.6.2 ROUGH DATABASES AND SECURITY MEASURES 150
8.7 EXAMPLE O F USE O F ROUGH SPATIAL DATA 152
8.8 CONCLUSION 154
REFERENCES 154
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XLII CONTENTS
9 ROUGH PRAGMATIC DESCRIPTION LOGIC 157
ZBIGNIEW BONIKOWSKI, EDWARD BRYNIARSKI, URSZULA WYBRANIECSKARDOWSKA 9.1
INTRODUCTION 158
9.2 THE PRAGMATIC SYSTEM OF KNOWLEDGE REPRESENTATION 159
9.3 INFORMATION SYSTEMS 166
9.4 APPROXIMATION IN INFORMATION SYSTEMS 169
9.5 THE PROPOSED DESCRIPTION LOGIC - THE ROUGH PRAGMATIC DESCRIPTION
LOGIC 174
9.5.1 SYNTAX OF THE LANGUAGE RPL 176
9.5.2 THE DISTINGUISHED AXIOMS FOR RPDL 177
9.5.3 SEMANTICS O F THE LANGUAGE RPL 179
9.6 PROSPECTS O F BEING APPLIED IN RESEARCH INTO ARTIFICIAL INTELLIGENCE
181 REFERENCES 183
10 APPLICATION O F ROUGH SET THEORY TO SENTIMENT ANALYSIS OF MICROBLOG
DATA 185
CHIEN-CHUNG CHAN, KATHY J. LISZKA 10.1 INTRODUCTION 185
10.2 PROBLEM FORMULATION 188
10.3 KEY WORD DRIVEN SENTIMENT ANALYSIS 191
10.3.1 DATA COLLECTION AND PREPROCESSING 191
10.3.2 DIMENSIONALITY REDUCTION 193
10.3.2.1 GROUPING BY EQUAL WORD FREQUENCY 193
10.3.2.2 MANUAL GROUPING 193
10.3.3 GENERATION OF SENTIMENTAL APPROXIMATION SPACE 194 10.3.4 SUBJECT
SENTIMENT ANALYSIS 194
10.4 EXPERIMENTAL RESULTS 195
10.5 CONCLUSIONS 200
REFERENCES 200
11 RELATIONSHIPS FOR COST AND UNCERTAINTY O F DECISION TREES 203
IGOR CHIKALOV, SHAHID HUSSAIN, MIKHAIL MOSHKOV 11.1 INTRODUCTION 203
11.2 BASIC NOTIONS 204
11.2.1 DECISION TABLES AND DECISION TREES 205
11.2.2 COST FUNCTIONS 206
11.2.3 CONSTRUCTING THE GRAPH A { T ) 207
11.3 RELATIONSHIPS: COST VS. UNCERTAINTY 209
11.3.1 THE FUNCTION T Y J 210
11.3.2 COMPUTING THE RELATIONSHIP 210
11.3.3 EXPERIMENTAL RESULTS 212
11.3.4 TIC-TAC-TOE DATASET 212
11.3.5 LYMPHOGRAPHY DATASET 212
11.3.6 BREAST-CANCER DATASET 213
11.3.7 AGARICUS-LEPIOTA DATASET 214
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CONTENTS XLIII
11.4 RELATIONSHIPS: NUMBER O F NODES VS. DEPTH 214
11.4.1 COMPUTING THE RELATIONSHIPS 215
11.4.2 EXPERIMENTAL RESULTS 216
11.4.3 TIC-TAC-TOE DATASET 217
11.4.4 LYMPHOGRAPHY DATASET 218
11.4.5 BREAST-CANCER DATASET 219
11.4.6 HOUSE-VOTES-84 DATASET 219
11.4.7 AGARICUS-LEPIOTA DATASET 219
11.5 CONCLUSION 220
REFERENCES 221
12 THE IMPACT RULES O F RECOMMENDATION SOURCES FOR ADOPTION INTENTION OF
MICRO-BLOG BASED ON DRSA WITH FLOW NETWORK GRAPH . 223 YANG-CHIEH CHIN,
CHIAO-CHEN CHANG, CHIUN-SIN LIN, GWO-HSHIUNG TZENG
12.1 INTRODUCTION 224
12.2 REVIEW ON RECOMMENDATION SOURCES FOR ADOPTION INTENTION 225 12.2.1
MICRO-BLOG 225
12.2.2 ADOPTION INTENTION 226
12.2.3 RECOMMENDATION SOURCE 226
12.3 BASIC CONCEPTS OF THE DRSA AND FLOW NETWORK GRAPH ALGORITHM 226
12.3.1 DATA TABLE 227
12.3.2 APPROXIMATION OF THE DOMINANCE RELATION 227
12.3.3 EXTRACTION O F DECISION RULES 229
12.3.4 DECISION RULES BASED ON FLOW NETWORK GRAPH 229
12.4 AN EMPIRICAL EXAMPLE O F MICRO-BLOG 230
12.4.1 SELECTION VARIABLES AND DATA 230
12.4.2 RULES FOR THE INTENTION TO ADOPT MICRO-BLOG 231
12.4.3 THE FLOW NETWORK GRAPH 234
12.4.4 DISCUSSIONS AND MANAGERIAL IMPLICATIONS O F RESEARCH FINDINGS 235
12.5 CONCLUSIONS AND REMARKS 236
REFERENCES 237
13 PROVIDING FEEDBACK IN UKRAINIAN SIGN LANGUAGE TUTORING SOFTWARE . 241
M.V. DAVYDOV, I.V. NIKOLSKI, V.V. PASICHNYK, O.V. HODYCH, Y.M.
SHCHERBYNA 13.1 INTRODUCTION 242
13.2 PROBLEM FORMULATION 243
13.3 SYSTEM SETUP IN THE NEW ENVIRONMENT 244
13.3.1 SOM-BASED IMAGE SEGMENTATION 244
13.3.2 FEEDFORWARD NEURAL NETWORK CLASSIFIER 249
13.3.3 INVERSE PHONG LIGHTING MODEL CLASSIFIER 251
13.4 HANDS AND FACE EXTRACTION 253
13.5 FEEDBACK DURING TUTORING 255
13.6 CONCLUSION 257
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XLIV CONTENTS
REFERENCES 259
14 HYBRID METHODS IN DATA CLASSIFICATION AND REDUCTION 263
PAWET DELIMATA, ZBIGNIEW SURAJ 14.1 INTRODUCTION 263
14.2 BASIC NOTIONS 266
14.2.1 INFORMATION SYSTEMS 266
14.2.2 CLASSICAL FC-NN METHOD 266
14.2.3 REDUCTS 267
14.2.4 LEAVE-ONE-OUT CROSS VALIDATION METHOD 267
14.2.5 BAGGING ALGORITHM 267
14.2.6 METRIC AND SINGLE CLASSIFIER 268
14.2.7 MEASURES O F DIVERSITY 268
14.2.8 DECISION RULES 269
14.2.9 LTF-C NEURAL NETWORK 270
14.2.10 DECOMPOSITION TREE 270
14.2.11 DETERMINISTIC AND INHIBITORY DECISION RULES 270
14.3 DATA REDUCTION METHODS 271
14.3.1 METHODOLOGY O F EXPERIMENTS AND RESULTS 276
14.4 FEATURE SELECTION METHODS 278
14.4.1 R B F S ALGORITHM 278
14.4.2 A R S ALGORITHM 280
14.4.3 METHODOLOGY OF THE EXPERIMENTS AND RESULTS 281
14.4.4 REDUCTS EVALUATION USING LAZY ALGORITHMS 283
14.4.5 METHODOLOGY OF THE EXPERIMENTS AND RESULTS 285
14.4.6 REDBOOST ALGORITHM 286
14.4.7 METHODOLOGY OF THE EXPERIMENTS AND RESULTS 288
14.5 MC2 MULTIPLE CLASSIFFIER SYSTEM 289
REFERENCES 290
15 UNCERTAINTY PROBLEM PROCESSING WITH COVERING GENERALIZED ROUGH SETS
293
JUN HU, GUOYIN WANG 15.1 INTRODUCTION 293
15.2 PRELIMINARY O F ROUGH SET THEORY 295
15.3 INCOMPLETE INFORMATION SYSTEM PROCESSING WITH COVERING GENERALIZED
ROUGH SETS 296
15.3.1 KNOWLEDGE REDUCTION MODEL O F COVERING APPROXIMATION SPACE 296
15.3.2 AN EXAMPLE 302
15.4 FUZZY DECISION MAKING WITH COVERING GENERALIZED ROUGH FUZZY SETS
303
15.4.1 COVERING GENERALIZED ROUGH FUZZY SETS 304
15.4.2 AN EXAMPLE 305
15.5 CONCLUSION 306
REFERENCES 307
IMAGE 7
CONTENTS XLV
16 HARDWARE IMPLEMENTATIONS O F ROUGH SET METHODS IN PROGRAMMABLE LOGIC
DEVICES 309
MACIEJ KOPCZYRISKI, JAROSTAW STEPANIUK 16.1 INTRODUCTION 309
16.2 SOLUTIONS ARCHITECTURE 310
16.2.1 PAWLAK'S IDEA O F ROUGH SET PROCESSOR 311
16.2.2 CELLULAR NETWORKS 312
16.2.2.1 SELF-LEARNING CELLULAR NETWORK 313
16.2.2.2 DIDACTIC EXAMPLE 314
16.2.3 DIRECT SOLUTIONS 318
16.3 CONCLUSIONS AND FUTURE RESEARCH 320
REFERENCES 321
17 DETERMINING COSINE SIMILARITY NEIGHBORHOODS BY MEANS O F THE
EUCLIDEAN DISTANCE 323
MARZENA KRYSZKIEWICZ 17.1 INTRODUCTION 323
17.2 BASIC NOTIONS AND PROPERTIES 324
17.2.1 BASIC OPERATIONS ON VECTORS AND THEIR PROPERTIES 324
17.2.2 VECTOR DISSIMILARITY AND SIMILARITY MEASURES 325
17.2.3 NEIGHBOURHOODS BASED ON DISSIMILARITY MEASURES 328 17.2.4
NEIGHBOURHOODS BASED ON SIMILARITY MEASURES 329
17.3 THE TRIANGLE INEQUALITY AS A MEAN FOR EFFICIENT DETERMINATION OF
NEIGHBORHOODS BASED ON A DISTANCE METRIC 330
17.3.1 EFFICIENT DETERMINATION O F E-NEIGHBORHOODS BASED ON A DISTANCE
METRIC 331
17.3.2 EFFICIENT DETERMINATION OF/C-NEIGHBORHOODS BASED ON A DISTANCE
METRIC 332
17.3.3 EFFICIENT DETERMINATION O F FC-NEAREST NEIGHBORS BASED ON A
DISTANCE METRIC 334
17.4 THE COSINE SIMILARITY MEASURE AND NEIGHBORHOODS VERSUS THE
EUCLIDEAN DISTANCE AND NEIGHBORHOODS 335
17.4.1 RELATIONSHIP BETWEEN THE COSINE SIMILARITY AND THE EUCLIDEAN
DISTANCE 335
17.4.2 VECTOR COSINE SIMILARITY NEIGHBORHOODS AND NORMALIZED VECTOR
NEIGHBORHOODS BASED ON THE EUCLIDEAN DISTANCE 336
17.4.3 VECTOR COSINE SIMILARITY NEIGHBORHOODS AND A-NORMALIZED VECTOR
NEIGHBORHOODS BASED ON THE EUCLIDEAN DISTANCE 338
17.5 DETERMINATION O F COSINE SIMILARITY NEIGHBORHOODS AS DETERMINATION
O F NEIGHBORHOODS BASED ON THE EUCLIDEAN DISTANCE 340 17.6 CONCLUSIONS
344
REFERENCES 344
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XLVI
CONTENTS
18 TIME VARIABILITY-BASED HIERARCHIC RECOGNITION O F MULTIPLE MUSICAL
INSTRUMENTS IN RECORDINGS 347
ELZBIETA KUBERA, ALICJA A. WIECZORKOWSKA, ZBIGNIEW W. RAS 18.1
INTRODUCTION 348
18.1.1 RANDOM FORESTS 350
18.1.2 OUTLINE O F THE PAPER 350
18.2 AUDIO DATA 351
18.2.1 HORNBOSTEL-SACHS SYSTEM O F MUSICAL INSTRUMENT CLASSIFICATION 352
18.3 FEATURE SET 353
18.4 EXPERIMENTS AND RESULTS 356
18.4.1 TRAINING AND TESTING OF RANDOM FORESTS 357
18.4.2 FEATURE-DRIVEN HIERARCHIC CLASSIFICATIONS OF MUSICAL INSTRUMENTS
357
18.5 SUMMARY AND CONCLUSIONS 362
REFERENCES 362
19 UNIFYING VARIABLE PRECISION AND CLASSICAL ROUGH SETS: GRANULAR
APPROACH 365
TSAU YOUNG LIN, YU RU SYAU 19.1 INTRODUCTION 365
19.2 NEIGHBORHOOD SYSTEMS (NS) 366
19.3 VARIABLE PRECISION ROUGH SETS 368
19.4 CONCLUSIONS 370
REFERENCES 371
20 FUZZY HYBRID MCDM FOR BUILDING STRATEGY FORCES 375
MEI-CHEN LO, GWO-HSHIUNG TZENG 20.1 INTRODUCTION 376
20.2 ABOUT STRATEGY FORCES (SF) 376
20.3 MEASURING THE FORCES TRACK 378
20.3.1 METHODOLOGIES 378
20.3.2 FUZZY THEORY WITH AHP AND ANP 379
20.3.3 FUZZY DECISION-MAKING 381
20.3.4 MAPPING TOOLS 382
20.3.4.1 DANP 382
20.3.4.2 VIKOR METHOD 384
20.4 EMPIRICAL CASE AND RESULTS 387
20.5 DISCUSSIONS AND IMPLICATIONS 390
20.6 CONCLUSION 391
20.7 FUTURE STUDY 391
REFERENCES 391
21 ROUGH SET-BASED FEATURE SELECTION: CRITERIA O F MAX-DEPENDENCY,
MAX-RELEVANCE, AND MAX-SIGNIFICANCE 393
PRADIPTA MAJI, SUSHMITA PAUL
IMAGE 9
CONTENTS
XLVII
21.1 INTRODUCTION 393
21.2 ROUGH SETS 395
21.3 RELATIONSHIPS O F MAX-DEPENDENCY, MAX-RELEVANCE, AND
MAX-SIGNIFICANCE 397
21.3.1 MAX-DEPENDENCY 397
21.3.2 MAX-RELEVANCE AND MAX-SIGNIFICANCE 398
21.4 MAXIMUM RELEVANCE-MAXIMUM SIGNIFICANCE ALGORITHM 400 21.4.1 THE
ALGORITHM 400
21.4.2 COMPUTATIONAL COMPLEXITY 401
21.4.3 GENERATION O F EQUIVALENCE CLASSES 402
21.5 MOLECULAR DESCRIPTOR SELECTION FOR FITTING QSAR MODEL 402
21.5.1 QSAR DATA SETS 403
21.5.2 SUPPORT VECTOR MACHINE 404
21.5.3 PERFORMANCE ANALYSIS 404
21.5.4 COMPARATIVE PERFORMANCE ANALYSIS 407
21.6 GENE SELECTION FROM MICROARRAY DATA 409
21.6.1 GENE EXPRESSION DATA SETS 410
21.6.2 IMPORTANCE O F ROUGH SETS 411
21.6.3 EFFECTIVENESS OF MRMS CRITERION 412
21.6.3.1 OPTIMUM VALUE O F FI 413
21.6.3.2 COMPARATIVE PERFORMANCE ANALYSIS 413
21.6.4 PERFORMANCE O F DIFFERENT ROUGH SET BASED ALGORITHMS . . 414 21.7
CONCLUSION 414
REFERENCES 415
22 TOWARDS LOGICS O F SOME ROUGH PERSPECTIVES O F KNOWLEDGE 419
A. MANI 22.1 INTRODUCTION 419
22.2 SOME BACKGROUND AND TERMINOLOGY 421
22.2.1 EQ(S) AND APPROXIMATIONS 423
22.2.2 RELATIVE CONSISTENCY O F KNOWLEDGE 424
22.3 SEMANTIC DOMAINS AND GRANULES 426
22.3.1 GRANULES 427
22.4 CONTAMINATION PROBLEM AND IPC 428
22.5 GENERALIZED MEASURES 431
22.6 ALGEBRAIC SEMANTICS-1 432
22.6.1 TOPOLOGY 436
22.7 ALGEBRAIC SEMANTICS AT META-C 436
22.7.1 ALGEBRAIC COMPUTATIONAL ASPECTS 440
22.8 ABSTRACTION 440
22.9 FURTHER DIRECTIONS 442
REFERENCES 442
23 CLASSIFIERS BASED ON NONDETERMINISTIC DECISION RULES 445
BARBARA MARSZAL-PASZEK, PIOTR PASZEK 23.1 INTRODUCTION 445
IMAGE 10
XLVIII
CONTENTS
23.2 BASIC NOTIONS 446
23.2.1 FIRST TYPE NONDETERMINISTIC RULES 448
23.2.2 SECOND TYPE NONDETERMINISTIC RULES 448
23.3 CLASSIFIERS 449
23.4 EXPERIMENTS 450
23.5 CONCLUSIONS 453
REFERENCES 454
24 APPROXIMATION AND ROUGH CLASSIFICATION O F LETTER-LIKE POLYGON SHAPES
455
ELISABETH RAKUS-ANDERSSON 24.1 INTRODUCTION 456
24.2 SAMPLED TRUNCATED .S'-FUNCTIONS IN THE APPROXIMATION OF LETTER-LIKE
POLYGONS 458
24.3 SAMPLED ^-FUNCTIONS OVER THE X-INTERVAL [0,1] 462
24.4 ROUGH SET THEORY IN POLYGON CLASSIFICATION 467
24.5 CONCLUSIONS 472
REFERENCES 473
25 ROUGH SET-BASED IDENTIFICATION O F HEART VALVE DISEASES USING HEART
SOUNDS 475
MOSTAFA A. SALAMA, OMAR S. SOLIMAN, ILIAS MAGLOGIANNIS, ABOUL ELLA
HASSANIEN, ALY A. FAHMY 25.1 INTRODUCTION 476
25.2 BACKGROUND INFORMATION 478
25.2.1 THE HEART VALVE DISEASES 478
25.2.2 ROUGH SETS: BASICS 480
25.3 THE PROPOSED ROUGH SET-BASED IDENTIFICATION O F HEART VALVE
DISEASES SYSTEM 481
25.3.1 PRE-PROCESSING PHASE 482
25.3.1.1 FEATURE REDUCTION 482
25.3.1.2 DISCRETIZATION BASED ON RSBR 483
25.3.2 ANALYSIS AND RULE GENERATING PHASE 483
25.3.2.1 IDENTIFICATION AND PREDICTION PHASE 484
25.4 EXPERIMENTAL RESULTS AND DISCUSSION 485
25.4.1 THE HEART SOUND: DATA SETS DECLARATION 485
25.4.2 ANALYSIS, RESULTS AND DISCUSSION 485
25.4.2.1 THE SET OF REDUCTS IN COMPARISON TO THE CHIMERGE FEATURE
SELECTION TECHNIQUE 485 25.4.2.2 THE SET O F EXTRACTED RULES 486
25.4.2.3 CLASSIFICATION ACCURACY OF THE PROPOSED MODEL IN COMPARISON TO
THE OTHER CLASSIFICATION TECHNIQUES 487
25.5 CONCLUSIONS 489
REFERENCES 489
IMAGE 11
CONTENTS
XLIX
26 ROUGH SETS AND NEUROSCIENCE 493
TOMASZ G. SMOLINSKI, ASTRID A. PRINZ 26.1 INTRODUCTION 494
26.2 BACKGROUND 494
26.2.1 THEORY O F ROUGH SETS 494
26.2.1.1 INFORMATION SYSTEMS AND DECISION TABLES . . . . 495 26.2.1.2
INDISCERNIBILITY 495
26.2.1.3 SET APPROXIMATION 495
26.2.1.4 REDUCTS 497
26.2.1.5 EXTENSIONS O F THE THEORY O F ROUGH SETS 497
26.2.2 NEUROSCIENCE 497
26.2.2.1 NEUROPHYSIOLOGY 498
26.2.2.2 BEHAVIORAL AND COGNITIVE NEUROSCIENCE 499 26.2.2.3
COMPUTATIONAL NEUROSCIENCE 500
26.2.2.4 NEUROLOGY 501
26.3 ROUGH SETS IN NEUROSCIENCE: SELECTED APPLICATIONS 502
26.3.1 CLINICAL NEUROLOGY 502
26.3.2 COGNITIVE COMPUTATION 503
26.3.3 CLASSIFICATORY DECOMPOSITION O F CORTICAL EVOKED POTENTIALS 504
26.3.4 CLASSIFICATION O F FUNCTIONAL AND NON-FUNCTIONAL NEURONAL MODELS
506
26.4 ROUGH SETS IN NEUROSCIENCE: OPEN PROBLEMS 510
26.5 CONCLUSIONS 510
REFERENCES 511
27 KNOWLEDGE REPRESENTATION AND AUTOMATED METHODS O F SEARCHING FOR
INFORMATION IN BIBLIOGRAPHICAL DATA BASES: A ROUGH SET APPROACH 515
ZBIGNIEW SURAJ, PIOTR GROCHOWALSKI, KRZYSZTOF PANCERZ 27.1 INTRODUCTION
516
27.2 BASIC CONCEPTS 517
27.2.1 ROUGH SETS 517
27.2.2 ONTOLOGIES 518
27.2.3 GENERATORS O F WEIGHTS 519
27.2.4 METRICS 520
27.2.5 ANGLE BETWEEN VECTORS 521
27.3 MAIN AIMS O F THE PAPER 521
27.4 THE RESULTS OBTAINED SO FAR 521
27.5 METHODS AND ALGORITHMS RELATED TO "INTELLIGENT" SEARCHING FOR
INFORMATION 522
27.5.1 METHODOLOGY O F CREATING A DETAILED ONTOLOGY 522
27.5.2 THE MECHANISM O F "INTELLIGENT" SEARCHING FOR INFORMATION 524
IMAGE 12
L CONTENTS
27.5.3 THE OUTLINE OF THE GENERAL ONTOLOGY FOR ROUGH SET THEORY AND ITS
APPLICATIONS 528
27.6 THE DESCRIPTION OF THE RSDS SYSTEM 529
27.6.1 THE LOGICAL STRUCTURE O F THE SYSTEM 530
27.6.2 THE FUNCTIONAL CAPABILITIES OF THE SYSTEM 531
27.7 EXPERIMENTS 534
27.7.1 METHODOLOGY OF EXPERIMENTS 534
27.7.2 COMMENTS 535
27.8 SUMMARY AND FINAL CONCLUSIONS 535
27.8.1 DIRECTIONS O F FURTHER RESEARCH 536
REFERENCES 536
28 DESIGN AND VERIFICATION O F RULE-BASED SYSTEMS FOR ALVIS MODELS . . .
. 539 MARCIN SZPYRKA, TOMASZ SZMUC 28.1 INTRODUCTION 539
28.2 EXAMPLE 541
28.3 HASKELL FORM O F RULE-BASED SYSTEMS 545
28.3.1 INPUT STATES 547
28.3.2 COMPLETENESS VERIFICATION 549
28.3.3 CONSISTENCY VERIFICATION 550
28.3.4 OPTIMALITY VERIFICATION 550
28.4 ALVIS MODELLING LANGUAGE 551
28.4.1 CODE LAYER 552
28.4.2 COMMUNICATION DIAGRAMS 552
28.4.3 SYSTEM LAYER 554
28.4.4 COMMUNICATION IN ALVIS 554
28.4.5 FORMAL VERIFICATION 555
28.5 RAILWAY TRAFFIC MANAGEMENT SYSTEM - CASE STUDY 555
28.6 SUMMARY 557
REFERENCES 558
29 ON OBJECTIVE MEASURES O F ACTIONABILITY IN KNOWLEDGE DISCOVERY . . .
559 LI-SHIANG TSAY, OSMAN GURDAL 29.1 INTRODUCTION 560
29.2 RELATED WORK 561
29.3 ACTIONABLE RULES 562
29.3.1 INFORMATION SYSTEMS 562
29.3.2 OBJECT-BASED ACTION RULE, LEFT SUPPORT, RIGHT SUPPORT, CONFIDENCE
563
29.4 OBJECTIVITY 564
29.5 THE STRAIGHTFORWARD STRATEGYGENERATOR APPROACH 565
29.5.1 EXPERIMENT I 570
29.5.2 EXPERIMENT II - HEPAR DATABASE 571
29.6 CONCLUSION 573
REFERENCES 573
IMAGE 13
CONTENTS LI
30 PSEUDOMETRIC SPACES FROM ROUGH SETS PERSPECTIVE 577
PIOTR WASILEWSKI 30.1 INTRODUCTION 577
30.2 ROUGH SETS AND INDISCERNIBILITY RELATIONS 578
30.3 PSEUDOMETRIC SPACES: DEFINITION, EXAMPLES AND BASIC PROPERTIES .
580 30.4 CONTINUITY 589
30.5 PSEUDOMETRICS DETERMINED BY FAMILIES O F SETS 591
30.6 PSEUDOMETRIZABILITY O F TOPOLOGICAL SPACES 594
30.7 EQUIVALENCE OF PSEUDOMETRIC SPACES 595
30.8 TOPOLOGICAL CHARACTERIZATION OF ATTRIBUTE DEPENDENCY 596
30.9 CONCLUSIONS 598
REFERENCES 599
INDEX 601 |
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| id | DE-604.BV040691568 |
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| indexdate | 2024-08-21T00:28:53Z |
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| isbn | 9783642303401 |
| language | English |
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| series | Intelligent systems reference library |
| series2 | Intelligent systems reference library |
| spelling | Rough sets and intelligent systems Professor Zdzisław Pawlak in memoriam 2 Andrzej Skowron and Zbigniew Suraj (ed.) Berlin [u.a.] Springer (2013) LI, 604 S. Ill., graf. Darst. txt rdacontent n rdamedia nc rdacarrier Intelligent systems reference library 43 Intelligent systems reference library ... (DE-588)4016928-5 Festschrift gnd-content Skowron, Andrzej 1943- (DE-588)1252627343 edt Pawlak, Zdzisław 1926-2006 (DE-588)108941482X hnr (DE-604)BV040691557 2 Erscheint auch als Online-Ausgabe 978-3-642-30341-8 Intelligent systems reference library 43 (DE-604)BV035704685 43 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=4017469&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025672299&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Rough sets and intelligent systems Professor Zdzisław Pawlak in memoriam Intelligent systems reference library |
| subject_GND | (DE-588)4016928-5 |
| title | Rough sets and intelligent systems Professor Zdzisław Pawlak in memoriam |
| title_auth | Rough sets and intelligent systems Professor Zdzisław Pawlak in memoriam |
| title_exact_search | Rough sets and intelligent systems Professor Zdzisław Pawlak in memoriam |
| title_full | Rough sets and intelligent systems Professor Zdzisław Pawlak in memoriam 2 Andrzej Skowron and Zbigniew Suraj (ed.) |
| title_fullStr | Rough sets and intelligent systems Professor Zdzisław Pawlak in memoriam 2 Andrzej Skowron and Zbigniew Suraj (ed.) |
| title_full_unstemmed | Rough sets and intelligent systems Professor Zdzisław Pawlak in memoriam 2 Andrzej Skowron and Zbigniew Suraj (ed.) |
| title_short | Rough sets and intelligent systems |
| title_sort | rough sets and intelligent systems professor zdzislaw pawlak in memoriam |
| title_sub | Professor Zdzisław Pawlak in memoriam |
| topic_facet | Festschrift |
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