Redundancy-Constrained feature selection with radial basis function networks

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2012, Pages

  Pal, Nikhil R ^a   Malpani, Mridul ^b  

^a INDIAN STATISTICAL INSTITUTE   (India)

^b INDIAN SCHOOL OF MINES   (India)

Author keywords

feature redundancy; feature selection; radial basis function (RBF) networks

Indexed keywords

BENCHMARK DATA; FEATURE REDUNDANCY; REDUNDANT FEATURES; REGRESSION PROBLEM;

FEATURE EXTRACTION; NEURAL NETWORKS; REDUNDANCY;

RADIAL BASIS FUNCTION NETWORKS;

EID: 84865063360     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2012.6252638     Document Type: Conference Paper

References (28)

1. N. R. Pal and K. K. Chintalapudi, A connectionist system for feature selection, Neural Parallel Sci. Comput., vol. 5, no. 3, pp. 359-381, 1997.
2. D. Chakraborty and N. R. Pal, Selecting Useful Groups of Features in a Connectionist Framework, IEEE Transactions on Neural Networks, vol 19, no 3, pp. 381-396, 2008.
3. Luping Zhou, Lei Wang, and Chunhua Shen, Feature Selection With Redundancy-Constrained Class Separability, IEEE Transactions on Neural Networks, vol 21 (5), 2010, pp 853 - 858
4. L. Yu, H. Liu, Efficient feature selection via analysis of relevance and redundancy, Journal of Machine Learning Research 5 (2004) 12051224.
5. L. Yu, H. Liu, Redundancy based feature selection for microarray data, in: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, p. 742.
6. Yaman Aksu, David J. Miller, George Kesidis, and Qing X. Yang, Margin-Maximizing Feature Elimination Methods for Linear and Nonlinear Kernel-Based Discriminant Functions, IEEE Transactions on Neural Networks, vol 21 (5), 2010, pp 701 - 717
7. H. Peng, F. Long, C. Ding, Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy, IEEE Transactions on PAMI (2005) 12261238.
8. S. Saleh, Y.E. Sonbaty, A feature selection algorithm with redundancy reduction for text classification, in: Computer and informa- tion sciences, 2007. ISCIS 2007. 22nd international symposium on Computer and Information Sciences, pp. 16.
9. D. Chakraborty and N. R. Pal, A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification, IEEE Trans. Neural Networks, Vol. 15, No. 1, pp. 110-123, 2004.
10. D. Chakraborty and N. R. Pal, Integrated feature analysis and fuzzy rule based system identification in a neuro-fuzzy paradigm, IEEE Trans. Syst., Man, Cybern. - B, Vol. 31, No. 3, pp. 391-400, 2001.
11. F. Kohavi and G. John, Wrappers for feature subset selection, Artificial intelligence, Vol. 97, No. 1, pp. 273-342, 1997.
12. D. Muni, N. R. Pal, and J. Das, Genetic programming for simultaneous feature selection and classifier design, IEEE Trans. Systems, Man and Cybern - B, Vol. 36, No. 1, pp. 106-117, 2006.
13. S. Niijima and S. Kuhara, Recursive gene selection based on maximum margin criterion: a comparison with SVM-RFE. BMC Bioinformatics, 7:543, 2006.
14. S. L. Chiu, Selecting input variables for fuzzy models, Journal of Intelligent & Fuzzy Systems, Vol. 4, No. 4, pp. 243-256, 1996.
15. T. Matsui, Y. Saruwatari, and M. Shigeno, An analysis of Dinkelbach's llgorithm for 0-1 fractional programming problems, Dept. Math. Eng. Inf. Phys., Univ. Tokyo, Tokyo, Japan, METR92-14, 1992.
16. C. Shen, H. Li, andM. J. Brooks, Supervised dimensionality reduction via sequential semidefinite programming, Pattern Recognit., vol. 41, no. 12, pp. 3644-3652, 2008.
17. N. R. Pal and S. Saha, Simultaneous structure identification and fuzzy rule generation for Takagi-Sugeno models, IEEE Trans. Syst., Man, Cybern. - B, Vol. 38, No. 6, pp. 1626-1638, 2008.
18. N. R. Pal, A fuzzy rule based approach to identify biomarkers for diagnostic classification of cancers, IEEE Int. Conf. Fuzzy Systems, Fuzz-IEEE 2007, pp. 1-6, 2007.
19. A. Saxena, N. R. Pal and M. Vora, Evolutionary Methods for Unsupervised Feature Selection Using Sammons Stress Function, Fuzzy Information and Engineering - An International Journal, Vol. 2, 3, pp 229-247, 2010, DOI 10.1007/s12543-010-0047-4.
20. A. Jain and D. Zongker, Feature selection: Evaluation, application, and small sample performance, ? IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 2, pp. 153-158, Feb. 1997.
21. H. Liu and L. Yu, Toward integrating feature selection algorithms for classification and clustering, IEEE Trans. Knowl. Data Eng., vol. 17, no. 4, pp. 491-502, Apr. 2005.
22. M. Last, A. Kandel, and O.Maimon, Information-theoretic algorithm for feature selection, Pattern Recognit. Lett., vol. 22, pp. 799-811, 2001.
23. J.C.Bezdek, J.Keller, R.Krishnapuram, and N.R.Pal, Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Norwell, MA : Kluwer, 1999.
24. Fakunaga, K. & Koontz, W. L. G.(1970), Application of the Karhunen-Loeve expansion to feature selection and ordering, IEEE Trans. Comput., 19, pp. 311-318
25. Devijver. P. A. & Kittler. J.(1982), Pattern Recognition, A statistical Approach, Prentice Hall International, Inc., London
26. Siedlick. W. & Sklansky.J.(1989), A note on genetic algorithms for large-scale feature selection, Pattern Recognition Letters, 10, pp. 335-347.
27. Ruck, D.W., Rogers S.K & Kabrisky, M.(1990), Feature Selection using a Multilayer Perceptron, Journal of Neural Network.
28. http://archive.ics.uci.edu/ml/