A multilayered neuro-fuzzy classifier with self-organizing properties

Fuzzy Sets and Systems

Volumn 159, Issue 23, 2008, Pages 3132-3159

  Mitrakis, N E ^a   Theocharis, J B ^a   Petridis, V ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

Author keywords

Classifiers combination; Decision fusion; Fuzzy classification; Hierarchical classifiers; Multilayered neuro fuzzy system; Structure learning

Indexed keywords

BENCHMARKING; CLASSIFIERS; DECISION SUPPORT SYSTEMS; EDUCATION; ENERGY ABSORPTION; FUSION REACTIONS; FUZZY SYSTEMS; GENETIC ALGORITHMS; KETONES; LEARNING ALGORITHMS; LEARNING SYSTEMS; NUCLEAR PHYSICS;

BENCHMARK CLASSIFICATION; CLASSIFICATION ACCURACIES; CLASSIFICATION TECHNIQUES; CLASSIFIERS COMBINATION; DATA SPLITTING; DECISION FUSION; DECISION SUPPORTS; DEGREE OF CONFIDENCE; FEATURE SELECTION; FEATURE TRANSFORMATIONS; FUSION OPERATOR; FUZZY CLASSIFICATION; FUZZY RULE-BASED SYSTEMS; HIERARCHICAL CLASSIFIERS; INFORMATIVE ATTRIBUTES; MULTI-LAYERED; MULTILAYERED NEURO-FUZZY SYSTEM; NEURO-FUZZY; NEW CLASS; PARAMETER LEARNING; SELF-ORGANIZING; STRUCTURE LEARNING;

CLASSIFICATION (OF INFORMATION);

EID: 52949109796     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fss.2008.01.032     Document Type: Article

References (32)

1. Benediktsson J.A., and Kanellopoulos I. Classification of multisource and hyperspectral data based on decision fusion. IEEE Trans. Geosci. Remote Sensing 37 3 (1999) 1367-1377
2. Benediktsson J.A., and Swain P.H. Consensus theoretic classification methods. IEEE Trans. Systems Man Cybernet. 22 4 (1992) 688-704
3. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms (1981), Plenum Press, New York
4. Z. Bingul, A. Sekmen, S. Zein-Sabbato, Evolutionary approach to multi-objective problems using adaptive genetic algorithms, in: Proc. 2000 IEEE Internet. Conf. on Systems Man and Cybernetics, TN, USA, 8-11 October 2000, pp. 1923-1927.
5. Cai L.Y., and Kwan H.K. Fuzzy classification using fuzzy inference networks. IEEE Trans. Systems Man Cybernet. (Part B) 28 3 (1998) 334-347
6. Casillas J., Cordon O., del Jesus M.J., and Herrera F. Genetric feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems. Inform. Sci. 136 1 (2001) 135-157
7. Castellano G., Fanalli A.M., and Mencar C. An empirical risk functional to improve learning in a neuro-fuzzy classifier. IEEE Trans. Systems Man Cybernet. 34 1 (2004) 725-731
8. Cho S.-B., and Kim J.H. Combining multiple neural networks by fuzzy integral for robust classification. IEEE Trans. Systems Man Cybernet. 25 2 (1995) 380-384
9. Cordon O., del Jesus M.J., and Herrera F. Genetic learning of fuzzy rule-based classification cooperating with fuzzy reasoning methods. Internat. J. Intell. Systems 13 10-11 (1998) 1025-1053
10. O. Cordon, F. Herrera, F. Hoffmann, L. Magdalena, Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases, in: Advances in Fuzzy Systems, World Scientific, Singapore, 2001.
11. del Jesus M.J., Hoffmann F., Navascues L.J., and Sanchez L. Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms. IEEE Trans. on Fuzzy Systems 12 3 (2004) 296-308
12. Delivopoulos E., and Theocharis J.B. A modified PNN algorithm with optimal PD modeling using the orthogonal least squares method. Inform. Sci. 168 1-4 (2004) 133-170
13. Drucker H., Cortes C., Jackel L.D., LeCun Y., and Vapnik V. Boosting and other ensemble methods. Neural Comput. 6 6 (1994) 1289-1301
14. Goncalves L.B., Velasco M.M.B.R., Pacheco M.A.C., and de Souza F.J. Inverted hierarchical neuro-fuzzy BSP system: a novel neuro-fuzzy model for pattern classification and rule extraction in databases. IEEE Trans. Systems Man Cybernet. (Part C) 36 2 (2006) 236-248
15. Gonzalez A., and Perez R. Selection of relevant features in a fuzzy genetic learning algorithm. IEEE Trans. Systems Man Cybernet. (Part B) 31 3 (2001) 417-425
16. Ho S.-Y., Chen H.-M., Ho S.-J., and Chen T.-K. Design of accurate classifiers with compact fuzzy-rule base using an evolutionary scatter partition of feature space. IEEE Trans. Systems Man Cybernet. (Part B) 34 2 (2004) 1031-1044
17. Hoffmann F. Combining boosting and evolutionary algorithms for learning of fuzzy classification rules. Fuzzy Sets and Systems 141 1 (2004) 47-58
18. Ishibuchi H., Nakashima T., and Murata T. Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Trans. Systems Man Cybernet. (Part B) 29 5 (1999) 601-618
19. Ivaknenko A.G. Polynomial theory of complex systems. IEEE Trans. Systems Man Cybernet. 1 (1971) 364-368
20. Kittler J., Hatef M., Duin R.P.W., and Matas J. On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20 3 (1998) 226-239
21. Kuncheva L.I., Bezdek J.C., and Duin R.P.W. Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recognition 34 2 (2001) 299-314
22. P.M. Murphy, D.W. Aha, UCI repository of machine learning databases, University of California, Department of Information and Computer Science, Irvine, CA 〈http://www.ics.uci.edu/-mlearn/MLRepository.htm〉, 1998.
23. Nauck D., and Kruse R. A neuro-fuzzy method to learn fuzzy classification rules from data. Fuzzy Sets and Systems 89 3 (1997) 277-288
24. Oh S.-K., and Pedrycz W. The design of self-organizing polynomial neural networks. Inform. Sci. 141 3 (2002) 237-258
25. Oh S.-K., Pedrycz W., and Ahn T.-C. Self-organizing neural networks with fuzzy polynomial neurons. Appl. Soft Comput. 2 1 (2002) 1-10
26. Papadakis S.E., and Theocharis J.B. A GA-based fuzzy modeling approach for generating TSK models. Fuzzy Sets and Systems 131 2 (2002) 121-152
27. Rutkowski L., and Cpalka K. Flexible Neuro-fuzzy systems. IEEE Trans. Neural Networks 14 3 (2003) 554-574
28. Setnes M., and Roubos H. GA-fuzzy modeling and classification: complexity and performance. IEEE Trans. Fuzzy Systems 8 5 (2000) 509-522
29. Takagi T., and Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Systems Man Cybernet. 15 1 (1985) 116-132
30. Wang J.-S., and Lee C.S.G. Self-adaptive neuro-fuzzy inference systems for classification applications. IEEE Trans. Fuzzy Systems 10 6 (2002) 790-802
31. Woods K., Kegelmeyer W.P., and Browyer K. Combination of multiple classifiers using local accuracy estimates. IEEE Trans. Pattern Anal. Mach. Intell. 19 (1994) 405-410
32. Zhang C.-X., and Zhang J.-S. A local boosting algorithm for solving classification problems. Comput. Statist. Data Anal. 38 4 (2008) 1928-1941