Applications of Fuzzy Sets Theory

front-matter......Page 1
Preliminaries......Page 16
A Fuzzy Representation Formalism......Page 17
Changes in the Set of Beliefs Caused by a New Belief......Page 18
Changes in the Fuzzy Desire Set Caused by a New Belief......Page 19
Comparing Fuzzy Sets of Desires......Page 20
Comparing Sets of Desires Under Qualitative Utility......Page 21
Goal Sets......Page 22
Conclusion......Page 23
Entropy of Abstract Discrete Probability Distributions......Page 24
Entropy and Co--entropy of Partitions......Page 25
Partitions Induced from Information Systems......Page 26
Partition Entropy on a Finite Measure Space......Page 27
Partitions as Identity Resolutions by Crisp Sets (Sharp Granulations)......Page 28
Fuzzy (Unsharp) Granulations......Page 29
Entropy and (Possible Negative) Co--entropy for Fuzzy Granulation......Page 31
A Normalized Non--negative Co--entropy for Fuzzy Granulation......Page 32
Conclusions and Open Problems......Page 33
Introduction......Page 35
Problem Definition......Page 36
Possibilistic Programming Model Development......Page 37
Model the Imprecise Data with Triangular Possibility Distribution......Page 39
Conclusion......Page 40
Introduction......Page 43
Procedural Semantics of Multi-adjoint Logic Programs......Page 44
Computational Cost Measures......Page 46
Reductants and Cost Measures......Page 48
Conclusions and Future Work......Page 50
Introduction......Page 52
Preliminaries......Page 53
Reaching Fixed Points for Multi-valued Functions on Multilattices......Page 54
Application to Fuzzy Logic Programs on a Multilattice......Page 56
Conclusions......Page 58
Introduction......Page 60
Issues in Knowledge Representation......Page 61
Contextualized Possibilistic Networks......Page 62
Temporal Framework Management......Page 63
Examples and Applications......Page 64
Conclusions......Page 66
Introduction......Page 68
The ARMS Algorithm......Page 70
Using the Suboptimal Mask......Page 71
Performances of the ARMS Algorithm......Page 72
Conclusions......Page 74
Introduction......Page 76
The Fuzzy System......Page 77
The Perturbed Fuzzy System......Page 78
The Derivatives Learning Algorithm......Page 81
Numerical Results......Page 82
References......Page 83
Introduction......Page 84
Uninorms - A Brief Summary......Page 85
Uninorms and Genetic Algorithms......Page 86
A Generic Construct of a Uninorm-Based Logic Neuron......Page 87
Experimental Results......Page 88
Conclusions......Page 90
Introduction......Page 92
Preliminaries......Page 94
Computing Similarity Measures......Page 96
A Tool to Visualize the Consensus State for Group Decision Making Problems......Page 97
Conclusions......Page 98
Reconstruction of Incomplete Fuzzy Preference Relations......Page 101
Numerical Simulations......Page 104
Final Remarks......Page 106
Introduction......Page 109
User Session Identification by Log Data Preprocessing......Page 110
User Profiling by Fuzzy Clustering......Page 112
Simulation Results......Page 113
Conclusions......Page 114
Introduction......Page 117
A Fuzzy Logic-Based PR System......Page 118
Data Fusion Methods......Page 121
Conclusions and Future Work......Page 123
Introduction......Page 125
When the Source is Known......Page 126
When the Source is Unknown......Page 127
When Information Is Corroborated Can It Help in Evaluating the Reliability of a Document?......Page 128
Date of the Document......Page 130
Conclusion......Page 131
Introduction......Page 133
Fuzzy Ontology......Page 134
Syntax......Page 135
Semantics......Page 136
Defining Fuzzy Ontology in KAON......Page 138
Conclusions......Page 139
Introduction......Page 142
Fuzzy Logic -Based Embedded KSSL Recognizer......Page 143
The Fusion Architecture for a Wireless PDA-Based MMFA......Page 145
An Improved Weight Decision Rule for Fusion and Fission......Page 146
Experiments and Results......Page 148
References......Page 149
Introduction......Page 151
Subsampling in Audio Signal......Page 152
Watermark Embedding......Page 153
Watermark Extraction......Page 155
Experimental Results......Page 156
Conclusions......Page 158
Introduction......Page 160
The DC*_1.1 Algorithm......Page 161
Experimental Results......Page 164
Final Remarks......Page 165
Introduction......Page 167
One Class Training FKNN......Page 168
Similarity Measures......Page 169
The Genetic-IFC......Page 170
Method Validation......Page 172
Conclusions......Page 174
The Problem of Choosing the Fuzzy Exponent......Page 176
Methods......Page 177
Simulated Data and Gene Expression Data Analyzed......Page 178
Results......Page 179
Discussion......Page 180
Conclusions......Page 182
Introduction......Page 185
Fuzzy Rule Based Classification Systems......Page 186
Rule Weights for Fuzzy Rules......Page 187
Preprocessing Imbalanced Data-Sets......Page 188
Data-Sets and Parameters......Page 189
Results and Analysis......Page 190
Concluding Remarks......Page 192
Introduction......Page 194
Function Approximation with Hinge Functions......Page 195
Hinge Search as an Optimization Problem......Page 196
Constrained Prototype Based FCRM......Page 197
Improvements of Hinge Identification......Page 198
Application Example......Page 199
Conclusion......Page 200
Introduction......Page 202
SVM, Discrete SVM and Fuzzy Discrete SVM......Page 203
Defining a Class Membership Function......Page 206
Computational Tests......Page 207
Introduction and Related Works......Page 210
Visualization......Page 211
Transitive Fuzzy Similarity Measure......Page 212
Application of the (Transitive) Fuzzy Similarity Measure......Page 213
Application Examples......Page 214
Conclusion......Page 216
Introduction......Page 218
Knowledge Presentation......Page 219
Significance Criterion Functions......Page 220
Effective Restrictions Method for Rule Generation......Page 222
Experiments......Page 224
Basic Notions......Page 226
Input Data......Page 227
"Ordewise" Polynomial Regression Model of Temperature......Page 228
Autoregressive Model with Fuzzy Data of Temperature......Page 229
Regression Model of Electricity Load as a Function of Temperature for December......Page 230
Outliers Detection......Page 231
Conclusions......Page 232
Introduction......Page 234
Possibilistic C-Means......Page 235
Possibilistic Clustering in Feature Space......Page 237
One-Cluster Possibilistic $C$-Means in Feature Space Algorithm......Page 238
Experimental Results and Discussion......Page 239
Conclusions......Page 240
Introduction......Page 242
Functional Structure......Page 243
Software Architecture......Page 244
Demonstrator Testbed......Page 246
Inter-Broker Connection Dynamics......Page 247
Discussion......Page 249
Introduction......Page 252
Binary Neuro-Fuzzy Networks......Page 253
Training BNFNs Using Quantum Computing......Page 254
Exhaustive Search by Nonlinear Quantum Circuits......Page 256
Conclusion......Page 258
Introduction......Page 260
Simplicial PWL Functions......Page 261
Proposed Algorithm......Page 262
Circuit Scheme and Hardware Implementation......Page 263
Conclusions......Page 267
Introduction......Page 268
Characterization of Energetic Flows......Page 269
Optimization by a Neuro-Fuzzy Control Unit......Page 270
Illustrative Tests......Page 272
Conclusion......Page 274
Motivation and Methodology......Page 276
Processing and Classification of the Olfactory Signal......Page 277
Results and Conclusion......Page 280
Introduction......Page 284
Support Vector Machines for Classification......Page 285
SVM Training on DSP-Based Architectures......Page 286
Training Algorithm: Reformulation and Optimization......Page 287
Training Algorithm: Basic Porting......Page 288
Experimental Results......Page 289
Introduction......Page 292
Robust Optimal Control......Page 293
Problem Formulation......Page 294
Control System for a Reconfiguration Manoeuvre......Page 295
Results......Page 297
Conclusions......Page 298
Preliminaires......Page 300
Collector of Entropies......Page 301
System of Functional Equations for the Collector......Page 302
Introduction......Page 306
A Brief Introduction to A-IFSs......Page 307
Szmidt and Kacprzyk's Entropy for A-IFSs......Page 308
Results......Page 309
Concluding Remarks......Page 311
Fuzzy Datalog......Page 313
Extensions of Fuzzy Datalog......Page 315
Extensions of Gödel Implication......Page 317
Bipolar Extension of Fuzzy Datalog......Page 318
Conclusions......Page 320
Introduction......Page 321
Combs’ URC Method......Page 322
Optimization......Page 323
Results......Page 325
References......Page 326
Introduction......Page 328
Related Works......Page 329
Influenceability and Its Algebraic Logical Framework......Page 330
Intuitionistic Fuzzy Influenceability......Page 331
Conclusions and Further Works......Page 333
References......Page 334
Introduction......Page 336
Generalized Intuitionistic Fuzzification -- From Images to Sets......Page 337
Intuitionistic Defuzzification: From A--IFSs to Images......Page 339
Parameter Selection Through Optimization of Image Fuzziness......Page 340
Experimental Results......Page 341
Conclusions......Page 342
Introduction......Page 343
IFIP Framework for Color Images......Page 344
Intuitionistic Fuzzification of Color Images......Page 345
Intuitionistic Fuzzy Luminance Histogram Hyperbolization......Page 346
Experimental Results......Page 347
Conclusions......Page 348
Intelligent Video Analysis Systems......Page 350
3D Model-Based Target Tracking......Page 351
License Plate Recognition in Security Applications......Page 353
Mobile ANPR System......Page 354
Conclusions......Page 355
References......Page 356
Eigen Fuzzy Sets......Page 357
The Genetic Algorithm......Page 359
Experimental Results......Page 360
Conclusions......Page 362
Proposed Deinterlacing Method......Page 364
Measuring Fuzzy Metrics......Page 365
Proposed Spatial Deinterlacing Method......Page 366
Conclusion......Page 367
Introduction......Page 370
Novel Fuzzy Metric and Proposed Filtering......Page 372
Experimental Results......Page 374
Conclusions......Page 375
Introduction......Page 377
Texture Feature Extraction......Page 378
Spatially Constrained Clustering......Page 379
Results and Conclusions......Page 381
Introduction......Page 385
The Main Functional Components of the Shape Retrieval System......Page 387
Indexing Function of the Shape Characteristics......Page 388
Partial Matching Functions of Sets of Descriptors......Page 389
Experiment......Page 390
Conclusion......Page 391
Introduction......Page 393
$E^2D$-$HUM$ Pre-processing......Page 394
Preventing the Halo Artifact Using Fuzzy C-Means......Page 395
Image Segmentation......Page 396
Results Evaluation and Measures......Page 397
Conclusions......Page 398
Introduction......Page 400
Preliminaries......Page 401
Algebraic Dilation and Erosion of Bipolar Fuzzy Sets......Page 402
Morphological Erosion of Bipolar Fuzzy Sets......Page 403
Morphological Dilation of Bipolar Fuzzy Sets......Page 404
Properties and Interpretation......Page 405
Conclusion......Page 407
Introduction......Page 409
CBIR Reference Scheme......Page 410
Embedding of Uncertainty About Color in CBIR......Page 411
Performance Measures......Page 412
Performance Evaluation......Page 413
Conclusions and Ongoing Work......Page 416
Introduction......Page 419
The EvCA for Edge Detection (EvCA-ED)......Page 420
Experimental Results......Page 422
Conclusion......Page 425
Incongruity Theory......Page 427
Semantic Script-Based Theory of Humour......Page 428
General Theory of Verbal Humour......Page 429
Computational Humour......Page 430
Humour Recognition......Page 431
Sociology......Page 432
Applications of Humour......Page 433
Conclusions......Page 434
Introduction......Page 437
Text Classification and the Voting Methods......Page 438
Text Classification Using a Semantic Grading......Page 440
Case Study: Classification of Conference Abstracts......Page 441
Conclusions......Page 443
Introduction......Page 445
Indian Language CLIR System Architecture......Page 447
Experiments......Page 448
Results......Page 449
Conclusion and Future Work......Page 451
Introduction......Page 453
Experimentation Framework......Page 454
Collections Description......Page 455
Preprocessing and Translation Heuristics......Page 456
Conclusions and Future Work......Page 460
Introduction......Page 462
JRC-Acquis and the Aligned Wordnets......Page 463
Word Alignment and Word Sense Disambiguation......Page 465
Concept-Based Text Classification......Page 468
Conclusions and Further Work......Page 469
Introduction......Page 471
NTCIR6 Opinion Analysis Pilot Task......Page 472
Corpus......Page 474
Evaluation......Page 475
Conclusions......Page 477
Introduction......Page 479
Corpus Construction......Page 480
Experiments and Results......Page 481
Conclusions and Further Work......Page 483
Introduction......Page 484
Text Similarity......Page 485
Ambiguity......Page 486
Idiomatic Expressions......Page 487
Evaluation......Page 488
Conclusions......Page 490
Introduction......Page 492
Script Representation......Page 493
Script Opposition......Page 494
Implementation......Page 495
Conclusion......Page 497
Dataset Complexity Measures......Page 499
Datasets......Page 500
Complexity Estimation......Page 501
Performance Evaluation of NN Classifiers and Their Ensembles......Page 502
Conclusion......Page 505
Introduction......Page 506
The Hybrid GMM/SVM Architecture......Page 507
Experimental Results......Page 508
Comparison with Other Methods......Page 509
Conclusion......Page 510
Introduction......Page 512
Graph Properties......Page 514
Enrichment and Z Score......Page 515
Results......Page 516
CAFASP4......Page 517
Introduction......Page 520
Problem Description......Page 521
Supervised Alignment by Canonical Correlation Analysis......Page 522
Experiments......Page 524
Conclusion......Page 525
Introduction......Page 527
Generative Kernel Functions......Page 529
Data and Experimental Results......Page 532
Discussion......Page 534
Introduction......Page 535
Live Wire Segmentation Approaches......Page 536
Gray Level Based Liver Segmentation......Page 537
Model Fitting......Page 539
Level Set Approaches......Page 540
Conclusions......Page 541
Introduction......Page 544
Space Filling Curves and Wavelets......Page 545
Clustering with the Z-Curve......Page 546
Experiments and Evaluation......Page 547
Conclusions and Future Directions......Page 549
Introduction......Page 552
Fuzzy Ensemble Clustering Based on Random Projections......Page 553
Experimental Environment......Page 555
Results......Page 556
Conclusions......Page 557
Introduction......Page 559
Numerical Encoding......Page 560
Experiments......Page 563
Conclusions......Page 564
Introduction......Page 566
The Dataset......Page 567
Entropy-Based Subclustering Similarity......Page 568
Feature Analysis of the Dataset......Page 569
Conclusions and Future Studies......Page 570
Introduction......Page 572
Mathematical Model for Gene Expression Data......Page 573
Golub's Method (GOLUB) [1]......Page 574
Results......Page 575
Introduction......Page 578
Feature Extraction and Denoising with the Bi-orthogonal Discrete Wavelet Transform......Page 579
Fuzzy Labeled Self Organizing Map......Page 580
Analysis with SOM and FLSOM......Page 581
Conclusions......Page 584
Introduction......Page 586
Background......Page 587
The Proposed Approach......Page 588
Experimental Results......Page 590
Discussion and Future Work......Page 592
Introduction......Page 595
Understanding Feature Selection by LIKNON......Page 596
Formal Analysis......Page 597
Main Results on Microarray Datasets......Page 599
Conclusions......Page 601
Introduction......Page 603
Method......Page 605
Experiment......Page 606
Results......Page 607
Conclusions......Page 608
Introduction......Page 611
Wavelet Analysis......Page 612
Genetic Algorithm for Feature Selection......Page 613
Results......Page 614
Conclusions......Page 617
References......Page 618
Introduction......Page 619
Protein Tyrosine Kinases Classification......Page 620
Results and Discussion......Page 621
Conclusions......Page 625
Introduction......Page 627
Modeling Gene Regulatory Networks with Bayesian Networks......Page 628
Evolving DAG Structures......Page 629
Results and Discussion......Page 630
Evaluation of the Evolutionary Approach Enhanced by Deterministic Crowding......Page 631
Comparison with Other Approaches......Page 632
Conclusions and Perspectives......Page 633
Introduction......Page 635
Modeling Immune System - Cancer - Vaccine Competition......Page 636
Search for an Optimal Schedule......Page 637
Conclusions and Perspectives......Page 640
Introduction......Page 642
Molecular Replacement......Page 644
Proposed Methodology......Page 645
Results and Discussion......Page 647
Conclusions......Page 648
Introduction......Page 650
Mass Spectrometry Data......Page 651
SpectraViewer......Page 652
Spectra Visualization......Page 653
Spectra Conversion......Page 655
Related Work......Page 656
Conclusions and Future Work......Page 657
Introduction......Page 658
Functional Features of S4......Page 659
Software Design of S4......Page 660
S4 Decision Tree......Page 661
Sample Sessions......Page 662
Significance and Impact......Page 664
Future Work......Page 665
Introduction......Page 666
Describing Transcription Factor Binding Sites......Page 667
Predicting Transcription Factor Binding Sites......Page 668
The Algorithm......Page 669
Experimental Evaluation......Page 670
Conclusions......Page 672
Introduction......Page 674
Fully Non homogeneous Hidden Markov Model......Page 675
The Proposed Model: Connection Between FNH-HMMs......Page 676
Experimental Results......Page 678
Introduction......Page 681
Multi-relational Learning for Structural Signatures of Proteins......Page 682
The Symbolic-Statistical Framework PRISM......Page 684
PRISM Modeling of Structural Signatures......Page 685
Conclusions and Future Work......Page 687
Introduction......Page 689
Methods......Page 690
Results......Page 691
Discussion and Conclusions......Page 693
Introduction......Page 697
Methods......Page 699
Discussion and Conclusions......Page 701
back-matter......Page 705