Rough set theory is a new soft computing tool which deals with vagueness and uncertainty. It has attracted the attention of researchers and practitioners worldwide, and has been successfully applied to many fields such as knowledge discovery, decision support, pattern recognition, and machine learning.
Rough Computing: Theories, Technologies and Applications offers the most comprehensive coverage of key rough computing research, surveying a full range of topics from granular computing to pansystems theory. With its unique coverage of the defining issues of the field, this commanding research collection provides libraries with a single, authoritative reference to this highly advanced technological topic.
Author(s): Aboul Ella Hassanien, Aboul Ella Hassanien, Zbigniew Suraj, Dominik Slezak, Pawan Lingras
Edition: illustrated edition
Publisher: IGI Global
Year: 2007
Language: English
Pages: 314
COVER PAGE ......Page 1
TITLE PAGE......Page 2
ISBN 1599045524......Page 3
IN MEMORIAM ......Page 4
TABLE OF CONTENTS (with page links)......Page 5
DETAILED TABLE OF CONTENTS ......Page 7
PREFACE ......Page 11
ACKNOWLEDGMENT ......Page 14
SECTION I FOUNDATIONS OF ROUGH SETS ......Page 15
INTRODUCTION ......Page 16
REPRESENTATION OF INFORMATION ......Page 18
KNOWLEDGE REPRESENTATION IN ROUGH SET THEORY ......Page 24
CLASSICAL (EQUIVALENCE) APPROXIMATION SPACES ......Page 26
TOLERANCE APPROXIMATION SPACES ......Page 29
ALGEBRAIC STRUCTURES RELATED TO ROUGH SETS ......Page 32
BOOLEAN ALGEBRAS AND ROUGH SETS ......Page 33
STONE ALGEBRAS AND ROUGH SETS ......Page 35
HEYTING ALGEBRAS AND TOLERANCE ROUGH SETS ......Page 37
CONCLUSION ......Page 45
REFERENCES ......Page 46
INTRODUCTION ......Page 53
BOOLEAN ALGEBRA ......Page 54
BOOLEAN FUNCTIONS IN BINARY BOOLEAN ALGEBRA ......Page 55
MONOTONE BOOLEAN FUNCTION ......Page 57
APPROXIMATE BOOLEAN REASONING METHODOLOGY ......Page 58
ROUGH SETS AND BOOLEAN REASONING APPROACH TO ATTRIBUTE SELECTION ......Page 62
EXAMPLE ......Page 65
DECISION RULE INDUCTION ......Page 67
EXAMPLE ......Page 68
DISCRETIZATION METHOD BASED ON ROUGH SET AND BOOLEAN REASONING ......Page 69
APPLICATIONS OF ROUGH SETS AND BOOLEAN REASONING METHODOLOGY IN DATA MINING ......Page 70
DECISION TREES ......Page 71
MD ALGORITHM FOR DECISION-TREE INDUCTION ......Page 73
SOFT CUTS AND SOFT DECISION TREES ......Page 74
ASSOCIATION ANALYSIS ......Page 75
SEARCHING FOR OPTIMAL ASSOCIATION RULES BY ROUGH SET METHODS ......Page 76
EXAMPLE ......Page 78
REFERENCES ......Page 80
INTRODUCTION ......Page 85
DEPENDENCY FUNCTION-BASED APPROACHES ......Page 88
DISCERNIBILITY MATRIX-BASED APPROACHES ......Page 95
FUZZY EQUIVALENCE CLASSES ......Page 99
FUZZY-ROUGH QUICKREDUCT ......Page 101
FUZZY ENTROPY-GUIDED FRFS ......Page 104
TOLERANCE ROUGH SETS ......Page 106
ALTERNATIVE SEARCH MECHANISMS GA-BASED APPROACHES ......Page 109
SIMULATED ANNEALING-BASED ......Page 110
ANT COLONY OPTIMIZATION BASED ......Page 112
PARTICLE SWARM OPTIMIZATION-BASED ......Page 115
CONCLUSION ......Page 118
REFERENCES ......Page 119
INTRODUCTION ......Page 123
FORMAL CONTEXTS AND MODAL-STYLE DATA OPERATORS ......Page 124
MODAL-STYLE DATA OPERATORS ......Page 125
CONNECTIONS OF MODAL-STYLE DATA OPERATORS ......Page 126
FORMAL CONCEPT ANALYSIS ......Page 127
ROUGH-SET ANALYSIS ON ONE UNIVERSE ......Page 128
ROUGH-SET ANALYSIS ON TWO UNIVERSES ......Page 130
COMPARATIVE STUDIES OF ROUGH-SET ANALYSIS AND FORMAL CONCEPT ANALYSIS ......Page 131
OBJECT-ORIENTED CONCEPT LATTICE ......Page 132
ATTRIBUTE REDUCTIONS IN CONCEPT LATTICES ......Page 133
APPROXIMATIONS BASED ON EQUIVALENCE CLASSES ......Page 135
APPROXIMATIONS BASED ON LATTICE-THEORETIC OPERATORS ......Page 136
APPROXIMATIONS BASED ON SET-THEORETIC OPERATORS ......Page 137
REFERENCES ......Page 138
SECTION II CURRENT TRENDS AND MODELS ......Page 143
INTRODUCTION ......Page 144
ROUGH SETS IN DATABASE DESIGN ......Page 145
ROUGH SETS APPLIED TO THE RELATIONAL DATABASE MODEL ......Page 146
ROUGH RELATIONAL OPERATORS ......Page 147
THE FUZZY ROUGH RELATIONAL DATABASE MODEL ......Page 150
FUZZY ROUGH RELATIONAL OPERATORS ......Page 151
INTUITIONISTIC SETS ......Page 153
INTUITIONISTIC ROUGH SETS ......Page 154
INTUITIONISTIC ROUGH RELATIONAL OPERATORS ......Page 156
INTUITIONISTIC ROUGH INTERSECTION ......Page 157
ROUGH SETS AND THE OBJECT-ORIENTED DATABASE MODEL ......Page 158
GENERALIZED OBJECT-ORIENTED DATABASE FRAMEWORK ......Page 159
ROUGH-SET OBJECT-ORIENTED DATABASE ......Page 160
FUZZY AND INTUITIONISTIC ROUGH OBJECT-ORIENTED DATABASE ......Page 162
CONCLUSION ......Page 164
REFERENCES ......Page 165
INTRODUCTION ......Page 167
ROUGH SET FLOW GRAPHS ......Page 168
INFERENCE IN ROUGH SET FLOW GRAPHS ......Page 170
TRADITIONAL ALGORITHM FOR RSFG INFERENCE ......Page 172
AN EFFICIENT ALGORITHM FOR RSFG INFERENCE ......Page 173
THE ORDER OF VARIABLE ELIMINATION ......Page 174
REFERENCES ......Page 176
INTRODUCTION ......Page 177
THE CLASSICAL INDEX ......Page 179
INDEX OF MUTUAL INFORMATION ......Page 180
COMBINED DOMINANCE ......Page 181
IMPORTANCE OF ATTRIBUTES FOR THE APPROXIMATION ......Page 182
EXAMPLES ......Page 183
PROBABILISTIC TRANSFORMATION ......Page 186
PROBABILISTIC SCORING ......Page 187
CONCLUSION ......Page 188
REFERENCES ......Page 189
INTRODUCTION ......Page 190
BACKGROUND ......Page 191
MAIN THRUST OF THE CHAPTER ......Page 193
CONCLUSION ......Page 198
REFERENCES ......Page 199
SECTION III ROUGH SETS AND HYBRID SYSTEMS ......Page 200
ABSTRACT ......Page 201
INTRODUCTION ......Page 202
ROUGH SETS: BASIC CONCEPTS ......Page 203
APPROXIMATION SPACES ......Page 204
ENVIRONMENT FOR LINE-CRAWLING ROBOT ......Page 205
MONOCULAR VISION SYSTEM TEST BED ......Page 206
MONOCULAR VISION SYSTEM ......Page 207
REINFORCEMENT LEARNING BY VISION SYSTEM ......Page 208
ACTOR CRITIC LEARNING METHOD ......Page 209
AVERAGE ROUGH COVERAGE ......Page 210
ROUGH COVERAGE ACTOR CRITIC METHOD ......Page 211
RUN-AND-TWIDDLE ACTOR CRITIC METHOD ......Page 212
RESULTS ......Page 213
LEARNING EXPERIMENTS IN A NOISY ENVIRONMENT ......Page 214
REFERENCES ......Page 216
ABSTRACT ......Page 219
INTRODUCTION ......Page 220
DECOMPOSITION TECHNIQUES IN SIGNAL CLASSIFICATION ......Page 221
THEORETICAL BACKGROUND THEORY OF ROUGH SETS ......Page 224
THEORY OF MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS ......Page 226
MODIFIED VECTOR EVALUATED GENETIC ALGORITHM (M-VEGA) ......Page 228
ELITIST NONDOMINATED VECTOR EVALUATED GENETIC ALGORITHM (END-VEGA) ......Page 229
RS AND MOEA-BASED CLASSIFICATORY SIGNAL DECOMPOSITION ......Page 230
FITNESS EVALUATION RECONSTRUCTION ERROR ......Page 231
CLASSIFICATION ACCURACY AND REDUCTION IN THE NUMBER OF COEFFICIENTS AND BASIS FUNCTIONS ......Page 232
GENERATION OF THE INITIAL POPULATION ......Page 233
CYLINDER-BELL-FUNNEL ......Page 235
SYNTHETIC CONTROL CHART ......Page 236
SETUP OF EXPERIMENTS ......Page 237
RESULTS ......Page 238
REFERENCES ......Page 240
INTRODUCTION ......Page 243
DISCRETIZATION ......Page 246
LERS ......Page 247
BELIEFSEEKER ......Page 249
RESULTS OF EXPERIMENTS ......Page 250
CONCLUSION ......Page 251
REFERENCES ......Page 252
INTRODUCTION ......Page 254
BASIC NOTIONS ......Page 256
DATA TABLES FOR DESCRIPTION OF CONCURRENT SYSTEMS ......Page 258
DESCRIPTION OF CONCURRENT SYSTEMS BY MEANS OF INFORMATION SYSTEMS ......Page 259
DESCRIPTION OF CONCURRENT SYSTEMS BY MEANS OF DYNAMIC INFORMATION SYSTEMS ......Page 260
DESCRIPTION OF CONCURRENT SYSTEMS BY MEANS OF DECOMPOSED INFORMATION SYSTEMS ......Page 264
EXTENSIONS OF INFORMATION SYSTEMS ......Page 268
NET MODELS ......Page 271
PREDICTION ......Page 276
THE ROSECON SYSTEM ......Page 279
EXPERIMENTS ......Page 280
CONCLUSION AND OPEN PROBLEMS ......Page 281
REFERENCES ......Page 282
COMPILATION OF REFERENCES ......Page 284
ABOUT THE CONTRIBUTORS ......Page 305
INDEX (with page links)......Page 312
