Fuzzy (c+p)-Means Clustering and Its Application to a Fuzzy Rule-Based Classifier: Toward Good Generalization and Good Interpretability

IEEE Transactions on Fuzzy Systems

Volumn 23, Issue 4, 2015, Pages 802-812

  Leski, Jacek M ^a  

^a SILESIAN UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Conditional fuzzy clustering; fuzzy classifier design; rule base with good interpretability

Indexed keywords

FUZZY CLUSTERING; FUZZY INFERENCE; FUZZY SETS; FUZZY SYSTEMS;

CONJUGATE GRADIENT ALGORITHMS; CRITERION FUNCTIONS; EXPERIMENTAL ANALYSIS; FUZZY C MEANS CLUSTERING; FUZZY CLASSIFIERS; FUZZY RULE-BASED CLASSIFIER; INTERPRETABILITY; MISCLASSIFICATION ERROR;

CLASSIFICATION (OF INFORMATION);

EID: 84938803955     PISSN: 10636706     EISSN: None     Source Type: Journal    
DOI: 10.1109/TFUZZ.2014.2327995     Document Type: Article

References (40)

1. P. P. Angelov and X. Zhou, "Evolving fuzzy-rule-based classifiers from data streams, " IEEE Trans. Fuzzy Syst., vol. 16, no. 6, pp. 1462-1475, Dec. 2008.
2. J. C. Bezdek, Pattern Recognition With Fuzzy Objective Function Algorithms. New York, NY, USA: Plenum, 1982.
3. E. Czogala and J. M. Leski, Fuzzy and Neuro-Fuzzy Intelligent Systems. Heidelberg, Germany: Physica-Verlag, 2000.
4. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis. New York, NY, USA: Wiley, 1973.
5. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. New York, NY, USA: Wiley, 2001.
6. H. Zheng, Z. X.Wang, L. C. Zhang, andQ.A.Wang "Clustering algorithm based on characteristics of density distribution, " in Proc. 2nd Int. Conf. Adv. Comput. Control, 2010, vol. 2, pp. 431-435.
7. P. J. Huber, Robust Statistics. New York, NY, USA: Wiley, 1981.
8. S. Haykin, Neural Networks: A Comprehensive Foundation. Upper Saddle River, NJ, USA: Prentice-Hall, 1999.
9. C.-W. Hsu, andC.-J. Lin, "A comparison of methods formulticlass support vector machines, " IEEE Trans. Neural Netw. vol. 13, no. 2, pp. 415-425, Mar. 2002.
10. B. Jiang, J. Pei, Y. F. Tao, and X.M. Lin, "Clustering uncertain data based on probability distribution similarity, " IEEE Trans. Knowl. Data Eng., vol. 25, no. 4, pp. 751-763, Apr. 2013.
11. L. Kuncheva, Fuzzy Classifiers. Heidelberg, Germany: Physica-Verlag, 2000.
12. J. M. Leski, "Ho-Kashyap classifier with generalization control, " Pattern Recog. Lett., vol. 24, pp. 2281-2290, 2003.
13. J. M. Leski, "An-margin nonlinear classifier based on if-then rules, " IEEE Trans. Syst., Man, Cybern. B, Cybern. vol. 34, no. 1, pp. 68-76, Feb. 2004.
14. J. M. Leski, "TSK-fuzzy modeling based on-insensitive learning, " IEEE Trans. Fuzzy Syst., vol. 13, no. 2, pp. 181-193, Apr. 2005.
15. J. M. Leski, "Iteratively reweighted least squares classifier and its-2-and-1-regularized kernel versions, " Bull. Polish Acad. Sci. Tech. Sci. vol. 58, no. 1, pp. 171-182, 2010.
16. J. M. Leski, and N. Henzel, "Generalized ordered linear regression with regularization, " Bull. Polish Acad. Sci. Tech. Sci. vol. 60, no. 3, pp. 481-489, 2012.
17. E. Lughofer and O. Buchtala, "Reliable all-pairs evolving fuzzy classifiers, " IEEE Trans. Fuzzy Syst., vol. 21, no. 4, pp. 625-641, Aug. 2013.
18. O. L. Mangasarian, and D. R. Musicant, "Lagrangian support vector machines, " J. Mach. Learn. Res. vol. 1, no. 1, pp. 161-177, 2001.
19. S. Mika, G. Rätsch, J. Weston, S. Schölkopf, and K.-R. Müller, "Fisher discriminant analysiswith kernels, " in Proc. Neural Netw. Signal Process., 1999, pp. 41-48.
20. J. K. Parker, and L. O. Hall, "Accelerating fuzzy C means using an estimated subsample size" IEEE Trans. Fuzzy Syst., 2014, to be published.
21. W. Pedrycz, "Conditional fuzzy clustering in the design of radial basis function neural network, " IEEE Trans. Neural Netw., vol. 9, no. 4, pp. 601-612, Jul. 1998.
22. W. Pedrycz, "Fuzzy set technology in knowledge discovery, " Fuzzy Sets Syst., vol. 98, pp. 279-290, 1998.
23. W. Pedrycz, "Proximity based clustering: A search for structural consistency in data with semantic blocks of features, " IEEE Trans. Fuzzy Syst., vol. 21, no. 5, pp. 978-982, Oct. 2013.
24. W. Pedrycz, "Cluster-centric fuzzy modeling, " IEEE Trans. Fuzzy Syst., 2014, to be published.
25. M. C. du Plessis, G. Niu, and M. Sugiyama, "Clustering unclustered data: Unsupervised binary labeling of two datasets having different class balances, " in Proc. Conf. Technol. Appl. Artif. Intell., 2013, pp. 1-6.
26. G. Rätsch, T. Onoda, and K.-R. Müller, "Soft margins for AdaBoost, " Mach. Learn., vol. 42, no. 3, pp. 287-320, 2001.
27. B. D. Ripley, Pattern Recognition and Neural Networks. Cambridge, U.K.: Cambridge Univ. Press, 1996.
28. J. A. Saez, J. Luengo, and F. Herrera, "On the suitability of fuzzy rulebased classification systems with noisy data, " IEEE Trans. Fuzzy Syst., 2014, to be published.
29. B. Schölkopf and A. J. Smola, Learning with Kernels. Support Vector Machines, Regularization, Optimization, and Beyond. London, U.K.: MIT Press, 2002.
30. J. A. K. Suykens and J.Vandewalle, "Least squares support vectormachine classifiers, " Neural Process. Lett., vol. 9, no. 3, pp. 293-300, 1999.
31. J. Tang, D. D. Li, Z. B. Zhou, L. Shu, D. Q. Zhang, andQ.Wang, "DC-tree: Density-based clustering index for objects in skewed distribution, " in Proc. IEEE 21st Int. Workshop Enabling Technol.: Infrastruct. Collaborative Enterp., 2012, pp. 336-341.
32. J. T. Tou and R. C. Gonzalez, Pattern Recognition Principles. London, U.K.: Addison-Wesley, 1974.
33. I. W. Tsang, J. T. Kwok, and P.-M. Cheung, "Core vector machines: Fast SVM training on very large data sets, " J. Mach. Learn. Res., vol. 6, no. 1, pp. 363-392, 2005.
34. I. W. Tsang, J. T. Kwok, and J. M. Zurada, "Generalized core vector machines, " IEEE Trans. Neural Netw., vol. 17, no. 5, pp. 1126-1140, Sep. 2006.
35. I. W. Tsang, A. Kocsor, and J. T. Kwok, "Large-Scale maximum margin discriminant analysis using core vectormachines, " IEEE Trans. Neur. Net., vol. 19, no. 4, pp. 610-623, Apr. 2008.
36. Y. Wang, L. Chen, and J.-P. Mei, "Incremental fuzzy clustering with multiple medoids for large data, " IEEE Trans. Fuzzy Syst., 2014, to be published.
37. A. Webb, Statistical Pattern Recognition. London, U.K.: Arnold, 1999.
38. V.Vapnik, Statistical Learning Theory. NewYork, NY, USA:Wiley, 1998.
39. X. Yang, G. Zhang, J. Lu, and J. Ma, "A kernel fuzzy c-means clusteringbased fuzzy support vector machine algorithm for classification problems with outliers or noises, " IEEE Trans. Fuzzy Syst., vol. 19, no. 1, pp. 105-115, Feb. 2011.
40. S.-M. Zhou and J. Q. Gan, "Constructing L2-SVM-based fuzzy classifiers in high-dimensional space with automatic model selection and fuzzy rule ranking, " IEEE Trans. Fuzzy Syst., vol. 15, no. 3, pp. 398-409, Jun. 2007.