Combination of one-class classifiers for multiclass problems by fuzzy logic

Neural Network World

Volumn 20, Issue 7, 2010, Pages 853-869

  Wilk, Tomasz ^b   Woźniak, Michal ^a  

^a WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

^b NONE

Author keywords

Fuzzy logic; Multiple classifier systems; One class classifiers; Pattern recognition

Indexed keywords

APPLICATION AREA; BENCHMARK DATASETS; BINARY CLASSIFIERS; COMBINING CLASSIFIERS; COMPUTER EXPERIMENT; FUZZY COMBINATION; FUZZY ENGINES; FUZZY INFERENCE SYSTEMS; INDIVIDUAL CLASSIFIERS; MATLAB ENVIRONMENT; MODEL SELECTION; MULTI-CLASS CLASSIFIER; MULTI-CLASS PROBLEMS; MULTIPLE CLASSIFIER SYSTEMS; ONE-CLASS CLASSIFIER; OPEN PROBLEMS; RESEARCH FRONTIERS; TRAINING ALGORITHMS;

DECISION MAKING; FUZZY INFERENCE; FUZZY SYSTEMS; PATTERN RECOGNITION SYSTEMS;

CLASSIFIERS;

EID: 79952130509     PISSN: 12100552     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (37)

1. Alexandre L. A., Campilho A. C, Kamel M.: Combining Independent and Unbiased Classifiers Using Weighted Average, Proc. of the 15th Internat. Conf. on Pattern Recognition, vol. 2, 2000, pp. 495-498.
2. Alpaydin M. E.: Combined 5x2cv F Test for Comparing Supervised Classification Learning Algorithms, Neural Computation, 11, 8, 1999, pp. 1885-1892.
3. Alpaydin M. E.: Introduction to Machine Learning. Second Edition, The MIT Press, Cambridge, MA, 2010.
4. Aarabi A., Fazel-Rezai R., Aghakhani Y.: A fuzzy rule-based system for epileptic seizure detection in intracranial EEG Clinical Neurophysiology, Clinical Neurophisiology, vol. 120, Issue 9, 2009, pp. 1648-1657.
5. Biggio B., Fumera G., Roli F.: Bayesian Analysis of Linear Combiners, Lecture Notes in Computer Science, vol. 4472, 2007, pp. 292-301. (Pubitemid 350270626)
6. Dietterich T. G., Bakiri G.: Solving multiclass learning problems via error-correcting output codes, Journal of Artificial Intelligence Research, 2, 1995, pp. 263-286.
7. Duan K., Keerthi S. S., Chu W.: Multi-Category Classification by Soft-Max Combination of Binary Classifiers. 4th International Workshop, 06, 11-13, 2003, pp. 125-134.
8. Duin R. P.: The Combining Classifier: to Train or Not to Train?, 16th International Conference on Pattern Recognition, IEEE Computer Society, ISBN 0-7695-1695-X, 2002, pp. 765-770. (Pubitemid 44844850)
9. Fumera G., Roli F.: A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems, IEEE Trans, on PAMI, 27, 6, 2005, pp. 942-956. (Pubitemid 40889844)
10. Giacinto G., Perdisci R., Del Rio M., Roli F.: Intrusion detection in computer networks by a modular ensemble of one-class classifiers. Information Fusion, 9, 2009, pp. 69-82. (Pubitemid 47589059)
11. Güler I., Übeyli E. D.: Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. Journal of Neuroscience Methods. 2005, 2, 148, pp. 113-121. (Pubitemid 41476221)
12. Hashem S.: Optimal linear combinations of neural networks, Neural Networks, 10, 4, 1997, pp. 599-614. (Pubitemid 27357174)
13. van der Heijden F., Duin R. P. W., de Ridder D., Tax D. M. J.: Classification, parameter estimation and state estimation - an engineering approach using Matlab, John Wiley and Sons, 2004.
14. Hong J., Min J., Cho U., Cho S.: Fingerprint classification using one-vs-all support vector machines dynamically ordered with naive Bayes classifiers. Pattern Recognition, 41, 2008, pp. 662-671. (Pubitemid 47503256)
15. Inoue H., Narihisa H.: Optimizing a Multiple Classifier Systems, LNCS, vol. 2417, 2002, pp. 285-294.
16. Jain A. K., Duin R. P. W., Mao J.: Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence., 1, 22, 2000, pp. 4-37. (Pubitemid 32132953)
17. Kittler J., Alkoot F. M.: Sum versus Vote Fusion in Multiple Classifier Systems, IEEE Trans, on Pattern Analysis and Machine Intelligence, 20, 2003, pp. 226-239.
18. Krzanowski W., Partrige D.: Software Diversity: Practical Statistics for its Measurement and Exploitation, Department of Computer Science, University of Exeter, 1996.
19. Lee C, Lin C: Multiple Compensatory Neural Fuzzy Networks Fusion Using Fuzzy Integral. Journal of Information Science and Engineering, 3, 23, 2007, pp. 837-851. (Pubitemid 46834705)
20. Marcialis G. L., Roli F.: Fusion of Face Recognition Algorithms for Video-Based Surveillance Systems. In: Foresti G. L., Regazzoni C, Varshney P. (Eds.), Multisensor Surveillance Systems: The Fusion Perspective, Kluwer Academic Publishers, 2003.
21. Klautau A., Jevtic N., Orlitsky A.: Combined Binary Classifiers With Application to Speech Recognition, Journal of Machine Learning Research, 4, 2003, pp. 1-15.
22. Kuncheva L. I., Bezdek J. C, Duin R. P. W.: Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recognition, 2, 2001, pp. 299-314. (Pubitemid 32871876)
23. Kuncheva L. I.: Combining Pattern Classifiers: Methods and Algorithms. New Jersey: Wiley, 2004.
24. Kurzyrński M., Burduk R.: Two-stage binary classifier with fuzzy-valued loss function. Pattern Analysis iz Applications, 9, 10, 2006, pp. 353-358. (Pubitemid 44757613)
25. Männle M.: Parameter Optimization for Takagi-Sugeno Fuzzy Models - Lessons Learnt. In: Proceedings of IEEE Systems Man and Cybernetics, Tucson (AZ), USA, October 2001, pp. 111-116. (Pubitemid 34210662)
26. Passerini A., Pontil M., Frasconi P.: New Results on Error Correcting Output Codes of Kernel Machines. Neural Networks, 15, 2004, pp. 45-54.
27. Rutkowski L.: Flexible Neuro-Fuzzy Systems. Structures, learning and performance evaluation, Kluwert, 2004.
28. Tax D. M. J., Duin R. P. W.: Combining One-class Classifiers. In: Proceedings of Multiple Classifier Systems, 2001, pp. 299-308. (Pubitemid 33303237)
29. Tax D. M. J., Duin R. P. W.: Using two-class classifiers for multiclass classification. Pattern Recognition Group, 2, 2002, pp. 124-127. (Pubitemid 44844696)
30. Tax D. M. J., Duin R. P. W.: Characterizing one-class datasets. Proceedings of the Sixteenth Annual Symposium of the Pattern Recognition Association of South Africa, 2005, pp. 21-26.
31. Tax D. M. J.: DDtools, the Data Description Toolbox for Matlab, version 1.7.3, Dec. 2009.
32. Tumer K., Ghosh J.: Analysis of Decision Boundaries in Linearly Combined Neural Classifiers, Pattern Recognition, 29, 1996, pp. 341-348. (Pubitemid 126397840)
33. Van Erp M., Vuurpijl L. G., Schomaker L. R. B.: An overview and comparison of voting methods for pattern recognition, Proc. of IWFHR.8, Canada, 2002, pp. 195-200.
34. Walkowiak T., Wilk T.: Incident Detection and Analysis in Communication and Information Systems by Fuzzy Logic, Dependability of Computer Systems, IEEE Computer Society, 2007, pp. 205-212. (Pubitemid 350080816)
35. Xu L., Krzyzak A., Suen Ch. Y.: Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition, IEEE Trans, on SMC, no. 3, 1992, pp. 418-435.
36. Zadeh L. A.: Fuzzy Sets and Applications: Selected Papers by Lotfi A. Zadeh. Information and Control, 8, 1965, pp. 338-358.
37. Zeng Q., Zhang L., Xu Y., Cheng L., Yan X., Zu J., Dai G.: Designing expert system for in situ Si3N4 toughened based on adaptive neural fuzzy inference system and genetic algorithms. Materials and Design, 30, 2009, pp. 256-259.