Rough sets and intelligent systems: Professor Zdzisław Pawlak in memoriam 1
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| Language: | English |
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Berlin [u.a.]
Springer
(2013)
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| Series: | Intelligent systems reference library
42 |
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| Physical Description: | LII, 646 S. Ill., graph. Darst. |
| ISBN: | 9783642303432 |
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| 245 | 1 | 0 | |a Rough sets and intelligent systems |b Professor Zdzisław Pawlak in memoriam |n 1 |c Andrzej Skowron and Zbigniew Suraj (ed.) |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c (2013) | |
| 300 | |a LII, 646 S. |b Ill., graph. Darst. | ||
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| 490 | 1 | |a Intelligent systems reference library |v 42 | |
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IMAGE 1
CONTENTS
1 PROFESSOR ZDZISLAW PAWLAK (1926-2006): FOUNDER O F THE POLISH SCHOOL O
F ARTIFICIAL INTELLIGENCE 1
ANDRZEJ SKOWRON, MIHIR KR. CHAKRABORTY, JERZY GRZYMALA-BUSSE, VICTOR
MAREK, SANKAR K. PAL, JAMES F. PETERS, GRZEGORZ ROZENBERG, DOMINIK
SLGZAK, ROMAN STOWINSKI, SHUSAKU TSUMOTO,
ALICJA WAKULICZ-DEJA, GUOYIN WANG, WOJCIECH ZIARKO 1.1 INTRODUCTION 2
1.2 BIOGRAPHY [51] 2
1.3 FROM THE CLOCK TO THE PSEUDO-RANDOM NUMBER GENERATOR 5
1.4 ENGINEER AND MATHEMATICIAN 6
1.5 COMPUTATION MODELS, ROUGH SETS AND ARTIFICIAL INTELLIGENCE 8
1.6 PROFESSOR PAWLAK'S INFLUENCE ON THE DEVELOPMENT O F COMPUTER SCIENCE
COMMUNITY 15
1.7 ZDZISLAW PAWLAK AND ARTIFICIAL INTELLIGENCE 31
1.8 PEOPLE AND NATURE 35
1.9 CONCLUSIONS 51
REFERENCES 53
2 LIST O F WORKS BY PROFESSOR ZDZISLAW PAWLAK (1926-2006) 57
ANDRZEJ SKOWRON PUBLICATIONS O F PROFESSOR ZDZISLAW PAWLAK 57
MANUSCRIPTS AND ARTICLES IN NEWSPAPERS 73
3 ROUGH SETS: FROM RUDIMENTS TO CHALLENGES 75
HUNG SON NGUYEN, ANDRZEJ SKOWRON 3.1 INTRODUCTION 76
3.2 VAGUE CONCEPTS 78
3.3 RUDIMENTS OF ROUGH SETS 79
3.3.1 INDISCERNIBILITY AND APPROXIMATION 79
3.3.2 DECISION SYSTEMS AND DECISION RULES 83
HTTP://D-NB.INFO/1021740659
IMAGE 2
X L I V
CONTENTS
3.3.3 DEPENDENCY O F ATTRIBUTES 85
3.3.4 REDUCTION OF ATTRIBUTES 86
3.3.5 DISCERNIBILITY AND BOOLEAN REASONING 87
3.3.6 ROUGH MEMBERSHIP 88
3.4 GENERALIZATIONS OF APPROXIMATION SPACES 9 0
3.5 ROUGH SETS AND INDUCTION 9 3
3.5.1 ROUGH SETS AND CLASSIFIERS 9 4
3.5.2 INDUCING RELEVANT APPROXIMATION SPACES 98
3.5.3 ROUGH SETS AND HIGHER ORDER VAGUENESS 101
3.6 INFORMATION GRANULATION 102
3.7 ONTOLOGICAL FRAMEWORK FOR APPROXIMATION 103
3.8 DISCERNIBILITY AND BOOLEAN REASONING: ROUGH SET METHODS FOR MACHINE
LEARNING, PATTERN RECOGNITION, AND DATA MINING . . . . 104 3.8.1 REDUCTS
IN INFORMATION AND DECISION SYSTEMS 106
3.8.2 ATTRIBUTE SELECTION 110
3.8.3 VALUE SET REDUCTION 112
3.8.3.1 DISCRETIZATION 112
3.8.3.2 SYMBOLIC ATTRIBUTE VALUE GROUPING 117
3.8.4 MINIMAL DECISION RULES 121
3.8.5 EXAMPLE: LEARNING O F CONCEPTS 124
3.8.6 ASSOCIATION RULES 126
3.9 ROUGH SETS, APPROXIMATE BOOLEAN REASONING AND SCALABILITY . . . .
131 3.9.1 REDUCT CALCULATION 131
3.9.2 DISCRETIZATION O F LARGE DATA SETS STORED IN RELATIONAL DATABASES
136
3.10 ROUGH SETS AND LOGIC 139
3.11 INTERACTIVE ROUGH GRANULAR COMPUTING (IRGC) 142
3.11.1 CONTEXT INDUCING AND IRGC 145
3.11.2 PROCESS MINING AND IRGC 149
3.11.3 PERCEPTION-BASED COMPUTING AND IRGC 151
3.12 CONCLUSIONS 154
REFERENCES 155
4 ZDZISLAW PAWLAK, DATABASES AND ROUGH SETS 175
VICTOR W. MAREK 4.1 INTRODUCTION 175
4.2 INFORMATION STORAGE AND RETRIEVAL SYSTEMS, DATABASES 179
4.3 ROUGHSETS 181
4.4 CONCLUSIONS 183
REFERENCES 184
5 J M A F - DOMINANCE-BASED ROUGH SET DATA ANALYSIS FRAMEWORK . . . . 1
85 JERZY BTASZCZYRISKI, SALVATORE GRECO, BENEDETTO MATARAZZO, ROMAN
SLOWINSKI, MARCIN SZELGG 5.1 INTRODUCTION 185
5.2 REMINDER ON THE DOMINANCE-BASED ROUGH SET APPROACH 186
IMAGE 3
CONTENTS XLV
5.2.1 DECISION TABLE 187
5.2.2 DOMINANCE CONES AS GRANULES O F KNOWLEDGE 188
5.2.3 APPROXIMATION O F ORDERED DECISION CLASSES 188
5.2.4 QUALITY O F APPROXIMATION 189
5.2.5 REDUCTION O F ATTRIBUTES 190
5.2.6 DECISION RULES 190
5.2.7 VARIABLE CONSISTENCY DOMINANCE-BASED ROUGH SET APPROACHES 193
5.3 EXAMPLE O F APPLICATION O F J M A F 193
5.3.1 RUNNINGJMAF 194
5.3.2 DECISION TABLE 194
5.3.3 DATA FILE 195
5.3.4 OPENING DATA FILE 196
5.3.5 CALCULATION O F DOMINANCE CONES 197
5.3.6 CALCULATION O F APPROXIMATIONS 198
5.3.7 CALCULATION OF REDUCTS 199
5.3.8 INDUCTION O F DECISION RULES 199
5.3.9 CLASSIFICATION 202
5.4 ROADMAP O F FUTURE DEVELOPMENT O F J M A F 204
5.5 EXEMPLARY APPLICATIONS O F DOMINANCE-BASED ROUGH SET APPROACH 204
5.6 GLOSSARY 205
REFERENCES 206
6 DYNAMIC PROGRAMMING APPROACH FOR EXACT DECISION RULE OPTIMIZATION 211
TALHA AMIN, IGOR CH.IK.ALOV, MIKHAIL MOSHKOV, BEATA ZIELOSKO 6.1
INTRODUCTION 211
6.2 IRREDUNDANT DECISION RULES 212
6.3 DIRECTED ACYCLIC GRAPH A(7) 215
6.4 PROCEDURE OF OPTIMIZATION RELATIVE TO LENGTH 217
6.5 PROCEDURE O F OPTIMIZATION RELATIVE TO COVERAGE 220
6.6 SEQUENTIAL OPTIMIZATION 221
6.7 EXPERIMENTAL RESULTS 224
6.8 CONCLUSIONS 226
REFERENCES 226
7 APPROACHES FOR UPDATING APPROXIMATIONS IN SET-VALUED INFORMATION
SYSTEMS WHILE OBJECTS AND ATTRIBUTES VARY WITH TIME 229
HONGMEI CHEN, TIANRUI LI, HONGMEI TIAN 7.1 INTRODUCTION 229
7.2 ROUGH SET THEORY IN A SET-VALUED INFORMATION SYSTEM 231
7.3 PRINCIPLE FOR DYNAMIC MAINTENANCE O F APPROXIMATIONS WHILE OBJECTS
AND ATTRIBUTES ARE ADDED SIMULTANEOUSLY 232
IMAGE 4
XLVI CONTENTS
7.4 ACCUMULATION PRINCIPLE FOR DYNAMIC MAINTENANCE OF
APPROXIMATIONS WHILE OBJECTS AND ATTRIBUTES ARE ADDED 238 7.5 EXAMPLE
240
7.6 PERFORMANCE ANALYSIS 244
7.7 CONCLUSIONS 246
REFERENCES 247
8 ON THE GRADUAL EVOLVEMENT O F THINGS 249
IVO DTINTSCH, GIINTHER GEDIGA 8.1 INTRODUCTION 249
8.2 THE SETUP 250
8.3 TRANSLATIONS 252
8.3.1 EVOLVING THINGS AND PROPERTIES 255
8.4 QUADTREES 256
8.5 CONCLUSION AND OUTLOOK 258
REFERENCES 259
9 AN EMPIRICAL COMPARISON O F RULE SETS INDUCED BY LERS AND
PROBABILISTIC ROUGH CLASSIFICATION 261
JERZY W. GRZYMATA-BUSSE, SHANTAN R. MAREPALLY, YIYU YAO 9.1 INTRODUCTION
261
9.2 ROUGH SETS AND THREE-WAY RULES 262
9.2.1 INDISCERNIBILITY RELATION 262
9.2.2 PAWLAK APPROXIMATIONS 263
9.2.3 PROBABILISTIC APPROXIMATIONS 264
9.2.4 THREE-WAY RULES 265
9.3 POSITIVE AND POSSIBLE RULE INDUCTION WITH LERS 267
9.4 RULES IN PROBABILISTIC ROUGH SETS 268
9.5 EXPERIMENTS 272
9.6 CONCLUSION 275
REFERENCES 275
10 EXPLORING NEIGHBORHOOD STRUCTURES WITH NEIGHBORHOOD ROUGH SETS IN
CLASSIFICATION LEARNING 277
QINGHUA HU, LEIJUN LI, PENGFEI ZHU 10.1 INTRODUCTION 277
10.2 PAWLAK'S ROUGH SETS AND RELATED WORKS 279
10.3 NEIGHBORHOOD ROUGH SETS 280
10.4 INFORMATION ENTROPY FOR NEIGHBORHOOD MODELS 288
10.5 BOUNDARY SAMPLE SELECTION WITH NEIGHBORHOOD MODEL 291
10.6 FEATURE SELECTION WITH NEIGHBORHOOD MODEL 294
10.7 RULE EXTRACTION WITH NEIGHBORHOOD MODEL 298
10.8 CONCLUSIONS AND FUTURE WORK 305
REFERENCES 305
IMAGE 5
CONTENTS
XLVII
11 ROUGH REPRESENTATIONS O F ILL-KNOWN SETS AND THEIR MANIPULATIONS
IN LOW DIMENSIONAL SPACE 309
MASAHIRO INUIGUCHI 11.1 INTRODUCTION 309
11.2 ILL-KNOWN SETS 311
11.3 POSSIBILITY AND NECESSITY MEASURES UNDER ILL-KNOWN SETS 320 11.4
CONCLUDING REMARKS 329
REFERENCES 330
12 PROPERTY-DRIVEN ROUGH SETS APPROXIMATIONS O F RELATIONS 333
RYSZARD JANICKI 12.1 INTRODUCTION 333
12.2 RELATIONS AND SOME OF THEIR BASIC CLASSIFICATIONS 335
12.3 CLASSICAL ROUGH RELATIONS 336
12.4 PROPERTY-DRIVEN ROUGH APPROXIMATIONS O F RELATIONS 337
12.5 PROPERTIES O F A-APPROXIMATIONS 342
12.6 COMPOSITE PROPERTIES 343
12.7 MIXED APPROXIMATIONS 346
12.8 APPROXIMATIONS BY PARTIAL ORDERS 347
12.9 APPROXIMATIONS BY EQUIVALENCE RELATIONS 351
12.10 FINAL COMMENT 355
REFERENCES 356
13 TOWARDS A COMPREHENSIVE SIMILARITY ANALYSIS O F VOTING PROCEDURES
USING ROUGH SETS AND SIMILARITY MEASURES 359
JANUSZ KACPRZYK, HANNU NURMI, SLAWOMIR ZADROZNY 13.1 INTRODUCTION 360
13.2 A BRIEF INTRODUCTION TO THE THEORY O F ROUGH SETS 362
13.3 A COMPARISON O F VOTING PROCEDURES 363
13.4 EQUIVALENT VOTING PROCEDURES AND INDISPENSABLE CRITERIA 367 13.5
SIMILARITY AND DISTANCES BETWEEN VOTING PROCEDURES 372
13.6 CONCLUDING REMARKS 377
REFERENCES 378
14 ALGEBRAS FOR INFORMATION SYSTEMS 381
MD. AQUIL KHAN, MOHUA BANERJEE 14.1 INTRODUCTION 382
14.1.1 TOWARDS AN ALGEBRA FOR INFORMATION SYSTEMS 384
14.2 ALGEBRA FOR DETERMINISTIC INFORMATION SYSTEMS 386
14.3 REPRESENTATION THEOREM FOR ABSTRACT DIS-ALGEBRAS 391
14.4 LOGICS FOR DETERMINISTIC INFORMATION SYSTEMS 393
14.5 ALGEBRA FOR INCOMPLETE INFORMATION SYSTEMS 394
14.6 ALGEBRA FOR NON-DETERMINISTIC INFORMATION SYSTEMS 395
IMAGE 6
XLVIII
CONTENTS
14.7 REPRESENTATION THEOREM FOR ABSTRACT NIS-ALGEBRAS AND
EQUATIONAL LOGIC FOR NISS 400
14.7.1 EXTENSION TO OTHER TYPES OF RELATIONS DEFINED ON NISS 403
14.8 CONCLUSIONS 405
REFERENCES 405
15 DNA ROUGH-SET COMPUTING IN THE DEVELOPMENT O F DECISION RULE REDUCTS
409
IKNO KIM, JUNZO WATADA, WITOLD PEDRYCZ 15.1 INTRODUCTION 409
15.2 DEOXYRIBONUCLEIC ACID 411
15.2.1 NITROGEN-CONTAINING BASES 411
15.2.2 PHOSPHODIESTER BONDS 412
15.2.3 HYDROGEN BONDS 412
15.3 DNA MOLECULAR TECHNIQUES 413
15.3.1 RESTRICTION ENZYME TECHNIQUE 414
15.3.2 LIGATION TECHNIQUE 416
15.3.3 POLYMERASE CHAIN REACTION TECHNIQUE 416
15.3.4 AFFINITY SEPARATION TECHNIQUE 417
15.3.5 GEL ELECTROPHORESIS TECHNIQUE 418
15.4 ROUGH SETS AND A MODEL DECISION TABLE 418
15.4.1 CONCEPT O F ROUGH SET THEORY 418
15.4.2 DECISION TABLE AS A MODEL 420
15.5 DNA ROUGH-SET COMPUTING 421
15.5.1 DIGRAPH IN DNA 421
15.5.2 ENCODING PROCESS IN DNA 424
15.6 EXPERIMENTAL STUDIES 429
15.6.1 EXPERIMENTS 429
15.6.2 EXPERIMENTAL RESULTS 433
15.7 CONCLUSIONS 435
REFERENCES 436
16 THREE-VALUED LOGIC FOR REASONING ABOUT COVERING-BASED ROUGH SETS 439
BEATA KONIKOWSKA 16.1 INTRODUCTION 439
16.2 COVERING-BASED ROUGH SETS 442
16.3 SUBORDINATION RELATION AND CLOSURE PROPERTIES OF APPROXIMATIONS AND
REGIONS 443
16.4 SYNTAX AND SEMANTICS O F THE LANGUAGE L C RS 4 4 5
16.4.1 SATISFACTION AND CONSEQUENCE RELATIONS FOR FORMULAS AND SEQUENTS
447
16.5 PROOF SYSTEM FOR THE LOGIC L L RS 4 4 8
16.6 SEQUENT CALCULUS CRS 448
IMAGE 7
CONTENTS X L I X
16.7 SOUNDNESS O F CRS 4 5 0
16.8 COMPLETENESS O F A SUBLANGUAGE 451
16.9 CONCLUSIONS 459
REFERENCES 460
17 MUSIC INFORMATION RETRIEVAL IN MUSIC REPOSITORIES 463
BOZENA KOSTEK 17.1 INTRODUCTION 463
17.2 REVIEW O F SELECTED SOLUTIONS IN TERMS O F MUSIC RECOMMENDATION 465
17.2.1 MUSIC RETRIEVAL WITHIN A CONTENT-BASED ANALYSIS 466 17.2.2
SYSTEMS USING A COMMUNITY INTERACTION 467
17.3 EXAMPLES O F MUSIC RECOMMENDATION SYSTEMS 468
17.3.1 PANDORA 468
17.3.2 LAST.FM 469
17.3.3 EXAMPLES O F OTHER SYSTEMS 4 7 0
17.4 MUSIC GENRE RECOGNITION EXPERIMENT 472
17.4.1 MUSIC DATABASE 472
17.4.2 MUSIC GENRE CLASSIFICATION 476
17.5 PROPOSAL FOR OBJECTIVIZATION OF ANNOTATION PROCESS 478
17.5.1 DESCRIPTION O F EXPERIMENT SETUP 478
17.5.2 PLATFORM FOR RUNNING THE EXPERIMENT 479
17.5.3 ANNOTATION-RELATED EXPERIMENT 481
17.5.3.1 RESULT ANALYSIS 485
17.6 CONCLUSIONS 486
REFERENCES 487
18 ROUGH SUPPORT VECTORS: CLASSIFICATION, REGRESSION, CLUSTERING 491
PAWAN LINGRAS, PARAG BHALCHANDRA, CORY BUTZ, S. ASHARAF 18.1
INTRODUCTION 491
18.2 ROUGH SUPPORT VECTOR MACHINES FOR MULTI-CLASSIFICATION 492
18.2.1 EXTENDING BINARY SVM'S FOR MULTI-CLASSIFICATION 494 18.2.1.1
ROUGH SET-BASED 1-V-L APPROACH 495
18.2.1.2 ROUGH SET-BASED 1 -V-R APPROACH 495
18.2.2 EXPERIMENTS WITH 1-V-L AND 1-V-R APPROACH 497
18.2.2.1 EXPERIMENTAL RESULTS USING 1-V-L 498
18.2.2.2 EXPERIMENTAL RESULTS USING 1-V-L 498
18.2.2.3 SEMANTICS O F ROUGH SET-BASED MULTI-CLASSIFICATION 499
18.3 DUAL ROUGH SUPPORT VECTOR REGRESSION 500
18.3.1 ROUGH PATTERNS 500
18.3.2 CONSERVATIVE AND AGGRESSIVE MODELING O F ROUGH PATTERNS 503
18.3.3 EMPIRICAL ANALYSIS O F DUAL RSVR 505
IMAGE 8
L CONTENTS
18.4 ROUGH SUPPORT VECTOR CLUSTERING 507
18.4.0.1 CLUSTER ASSIGNMENT 511
18.4.0.2 ROLE O F T ) AND 8 511
18.4.1 EXPERIMENTAL RESULTS WITH RSVC 512
18.4.1.1 SYNTHETIC DATA SET 512
18.4.1.2 WINE RECOGNITION DATA SET 512
18.5 SUMMARY AND CONCLUSIONS 513
REFERENCES 514
19 LOGIC-BASED ROUGHIFICATION 517
LINH ANH NGUYEN, ANDRZEJ SZALAS 19.1 INTRODUCTION 517
19.2 PRELIMINARIES 519
19.3 SIMILARITY-BASED ROUGHIFICATION 520
19.3.1 DEFINITIONS 520
19.3.2 PROPERTIES 521
19.3.3 SELECTED APPLICATIONS 522
19.4 RELATIONAL ROUGHIFICATION 523
19.4.1 DEFINITIONS 523
19.4.2 PROPERTIES 524
19.4.3 GRANULATING RELATIONAL DATABASES 526
19.5 TERMINOLOGICAL ROUGHIFICATION 526
19.5.1 DESCRIPTION LOGICS AND INFORMATION SYSTEMS 527
19.5.2 BISIMULATION AND INDISCERNIBILITY 531
19.5.3 CONCEPT LEARNING 533
19.5.4 BISIMULATION-BASED APPROXIMATION O F CONCEPTS 540 19.6
CONCLUSIONS 541
REFERENCES 542
20 HOW NEAR ARE ZDZISLAW PAWLAK'S PAINTINGS? 545
JAMES F. PETERS 20.1 INTRODUCTION 546
20.2 PRELIMINARIES 548
20.3 PERCEPTUAL INDISCERNIBILITY RELATION IN SEGMENTING PAINTINGS 550
20.4 NEIGHBOURHOODS IN PAINTINGS BY Z. PAWLAK 552
20.5 E-APPROACH NEARNESS 557
20.6 REGIONS-OF-INTEREST IN Z. PAWLAK'S PAINTINGS 561
20.7 CONCLUDING REMARKS 565
REFERENCES 566
21 AN IMPLEMENTATION O F THE ZDZISLAW PAWLAK IDEA FOR REASONING ABOUT
UNCERTAINTY: APPROXIMATE REASONING BY PARTS 569
LECH POLKOWSKI, MARIA SEMENIUK-POLKOWSKA 21.1 ABSTRACT 569
21.2 INTRODUCTION: THE LANGUAGE O F PARTS 570
21.3 A MEREOLOGICAL MODEL FOR ROUGH SETS 570
IMAGE 9
CONTENTS LI
21.4 APPROXIMATIONS 573
21.5 AN EXTENSION TO PARTS TO A DEGREE: ROUGH MEREOLOGY 574
21.5.1 ROUGH INCLUSIONS IN INFORMATION AND DECISION SYSTEMS 577
21.6 APPROXIMATIONS TO A DEGREE 579
21.7 A CHARACTERIZATION O F / JL 580
21.8 ACKNOWLEDGEMENT 582
21.9 CONCLUSIONS 582
REFERENCES 582
22 GRANULAR CONCEPT MAPPING AND APPLICATIONS 585
SUMALEE SONAMTHIANG, KANLAYA NARUEDOMKUL, NICK CERCONE 22.1 INTRODUCTION
585
22.2 RELATED STUDY 588
22.3 GRANULAR CONCEPT HIERARCHY 590
22.3.1 FORMAL DEFINITIONS O F A GRANULAR CONCEPT HIERARCHY . . . . 590
22.3.2 SYNTAX AND SEMANTICS OF A GRANULAR CONCEPT 591
22.4 GRANULAR CONCEPT HIERARCHY CONSTRUCTION 594
22.4.1 AN ALGORITHM FOR RECURSIVE GRANULATIONS 594
22.4.2 AN ALGORITHM FOR LEVEL-WISE ATTRIBUTE SELECTION 595 22.4.3
MAPPING FOR APPROPRIATE GRANULARITY IN A GCH 597
22.5 EVALUATION 600
22.6 CONCLUSION 601
REFERENCES 601
23 ROUGH SETS AND MEDICAL DIFFERENTIAL DIAGNOSIS 605
SHUSAKU TSUMOTO 23.1 INTRODUCTION 605
23.2 MEDICAL DIAGNOSTIC PROCESS 606
23.2.1 DIFFERENTIAL DIAGNOSIS O F HEADACHE 606
23.2.2 CLASSIFICATION O F HEADACHE 606
23.2.3 EXAMPLES: MIGRAINE AND TENSION-TYPE HEADACHE 607 23.2.4 FOCUSING
MECHANISM 608
23.3 DEFINITION OF RULES 609
23.3.1 ROUGH SETS 609
23.3.2 CLASSIFICATION ACCURACY AND COVERAGE 610
23.3.2.1 DEFINITION OF ACCURACY AND COVERAGE 610
23.3.3 PROBABILISTIC RULES 611
23.3.4 POSITIVE RULES 611
23.3.5 NEGATIVE RULES 612
23.4 ALGORITHMS FOR RULE INDUCTION 614
23.5 EXTENSION INTO VARIABLE PRECISION ROUGH SET MODEL 615
23.6 DISCUSSION: WHAT HAS NOT BEEN ACHIEVED? 616
23.7 CONCLUSION 617
REFERENCES 620
IMAGE 10
LI I CONTENTS
24 SCIENCE AND SEMANTICS: A NOTE ON ROUGH SETS AND VAGUENESS 623
MARCIN WOLSKI 24.1 INTRODUCTION 624
24.2 OBESE: A CASE STUDY 626
24.3 ROUGH SET THEORY 628
24.4 SUPERVALUATIONIST SEMANTICS 631
24.5 VAGUENESS IN SCIENCE 636
24.6 CONCLUSIONS 642
REFERENCES 642
INDEX 645 |
| any_adam_object | 1 |
| author2 | Skowron, Andrzej 1943- |
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| author2_variant | a s as |
| author_GND | (DE-588)1252627343 (DE-588)108941482X |
| author_facet | Skowron, Andrzej 1943- |
| building | Verbundindex |
| bvnumber | BV040691563 |
| ctrlnum | (OCoLC)828789896 (DE-599)DNB1021740659 |
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| genre_facet | Festschrift |
| id | DE-604.BV040691563 |
| illustrated | Illustrated |
| indexdate | 2024-08-21T00:28:53Z |
| institution | BVB |
| isbn | 9783642303432 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025672294 |
| oclc_num | 828789896 |
| open_access_boolean | |
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| owner_facet | DE-473 DE-BY-UBG |
| physical | LII, 646 S. Ill., graph. Darst. |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | Springer |
| record_format | marc |
| series | Intelligent systems reference library |
| series2 | Intelligent systems reference library |
| spelling | Rough sets and intelligent systems Professor Zdzisław Pawlak in memoriam 1 Andrzej Skowron and Zbigniew Suraj (ed.) Berlin [u.a.] Springer (2013) LII, 646 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Intelligent systems reference library 42 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 1 Erscheint auch als Online-Ausgabe 978-3-642-30344-9 Intelligent systems reference library 42 (DE-604)BV035704685 42 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=4017468&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=025672294&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 1 Andrzej Skowron and Zbigniew Suraj (ed.) |
| title_fullStr | Rough sets and intelligent systems Professor Zdzisław Pawlak in memoriam 1 Andrzej Skowron and Zbigniew Suraj (ed.) |
| title_full_unstemmed | Rough sets and intelligent systems Professor Zdzisław Pawlak in memoriam 1 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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