Selecting useful groups of features in a connectionist framework

IEEE Transactions on Neural Networks

Volumn 19, Issue 3, 2008, Pages 381-396

  Chakraborty, Debrup ^a   Pal, Nikhil R ^b  

^a DEPARTAMENTO DE FÍSICA   (Mexico)

^b INDIAN STATISTICAL INSTITUTE   (India)

Author keywords

Classification; Feature selection; Multilayered perceptron networks; Radial basis function (RBF) networks

Indexed keywords

APPROXIMATION THEORY; COMPUTER SIMULATION; FEATURE EXTRACTION; MULTILAYER NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS;

CONNECTIONIST SCHEMES; FUNCTION APPROXIMATION PROBLEM;

CLASSIFICATION (OF INFORMATION);

ARTICLE; ARTIFICIAL NEURAL NETWORK; HUMAN; INFORMATION RETRIEVAL; INFORMATION SYSTEM; ONLINE SYSTEM; SIGNAL PROCESSING;

HUMANS; INFORMATION STORAGE AND RETRIEVAL; INFORMATION SYSTEMS; NEURAL NETWORKS (COMPUTER); ONLINE SYSTEMS; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 40949142799     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2007.910730     Document Type: Article

References (49)

1. H. Al-Mubaid and N. Ghaffari, "Identifying the most significant genes from gene expression profiles for sample classification," in Proc. IEEE Conf. Granural Comput., 2006, pp. 655-658.
2. E. Anderson, "The irises of the Gaspe Peninsula," Bull. Amer. IRIS Soc., vol. 59, pp. 2-5, 1935.
3. L. M. Beleue and K. W. Bauer, "Determining input features for multilayered perceptron," Neurocomputing, vol. 7, no. 2, pp. 111-121, 1995.
4. 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.
5. C. Bhattacharyya, L. R. Grate, A. Rizki, D. Radisky, F. J. Molina, M. I. Jordan, M. J. Bissell, and I. S. Mian, "Simultaneous classification and relevant feature identification in high-dimensional spaces: Application to molecular profiling data," Signal Process., vol. 83, pp. 729-743, 2003.
6. C. L. Blake and C. J. Merz, "UCI Repository of Machine Learning Databases," Dept. Inf. Comput. Sci., Univ. California, Irvine, CA, 1998 [Online]. Available: http://www.ics.uci.edu/mlearn/MLRepository.html
7. A. L. Blum and P. Langley, "Selection of relevant features and examples in machine learning," Artif. Intell., vol. 97, no. 1, pp. 245-271, 1997.
8. F. Z. Brill, D. E. Brown, and W. N. Martin, "Fast genetic selection of features for neural network classifiers," IEEE Trans. Neural Netw. vol. 3, no. 2, pp. 324-328, Mar. 1992.
9. 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, Cybern., vol. 31, no. 3, pp. 391-400, Jun. 2001.
10. D. Chakraborty and N. R. Pal, "A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification," IEEE Trans. Neural Netw., vol. 15, no. 1, pp. 110-123, Jan. 2004.
11. D. Chakraborty and N. R. Pal, "Two connectionist schemes for selecting groups of features (sensors)," in Proc. 2003 IEEE Int. Conf. Fuzzy Syst., 2003, pp. 161-166.
12. R. De, N. R. Pal, and S. K. Pal, "Feature analysis: Neural network and fuzzy set theoretic approaches," Pattern Recognit., vol. 30, no. 10, pp. 1579-1590, 1997.
13. A. P. Engelbrecht, "A new pruning heuristic based on variance analysis of sensitivity information," IEEE Trans. Neural Netw., vol. 12, no. 6, pp. 1386-1399, Nov. 2001.
14. K. Fukunaga, Statistical Pattern Recognition. New York: Academic, 1989.
15. R. Haralick, K. Shanmugam, and I. Dinstein, "Texture features for image classification," IEEE Trans. Syst. Man Cybern., vol. SMC-3, no. 6, pp. 610-621, Nov. 1973.
16. E. J. Hartman, J. D. Keeler, and J. M. Kowalski, "Layered neural networks with Gaussian hidden units as universal approximators," Neural Computat., vol. 2, pp. 210-215, 1990.
17. E. Hewitt, Real and Abstract Analysis. Berlin, Germany: Springer-Verlag, 1965.
18. K. Hornik, "Some new results on neural network approximation," Neural Netw., vol. 6, pp. 1069-1072, 1993.
19. K. Hornik, "Approximation capabilities of multilayer perceptrons," Neural Netw., vol. 4, pp. 251-257, 1991.
20. K. Hornik, M. Stinchcombe, and H. White, "Universal approximation of an unknown mapping and its derivatives using multilayered feedforward networks," Neural Netw., vol. 3, pp. 551-560, 1990.
21. K. Hornik, M. Stinchcombe, and H. White, "Multilayered feedforward networks are universal approximators," Neural Netw., vol. 2, pp. 259-366, 1989.
22. A. Jain and D. Zongker, "Feature selection: Evaluation, application and small sample performance," IEEE Trans. Pattern Anal. Mach. Intell. vol. 19, no. 2, pp. 148-153, Feb. 1997.
23. I. T. Jollife, Principal Component Analysis. New York: Springer-Verlag, 1986.
24. D. Koller and M. Sahami, "Toward optimal feature selection," in Proc. 13th Int. Conf. Mach. Learn., 1996, pp. 284-292.
25. M. Kudo and J. Sklansky, "Comparison of algorithms that selects features for pattern classifiers," Pattern Recognit., vol. 33, no. 1, pp. 25-41, 2000.
26. A. S. Kumar, S. Chowdhury, and K. L. Mazumder, "Combination of neural and statistical approaches for classifying space-borne multispectral data," in Proc. Int. Conf. Adv. Pattern Recognit. Digit. Tech., Calcutta, India, 1999, pp. 87-91.
27. F. Kohavi and G. John, "Wrappers for feature subset selection," Artif. Intell., vol. 97, no. 1, pp. 273-342, 1997.
28. A. Laha, N. R. Pal, and J. Das, "Designing prototype-based classifiers and their application to multispectral satellite images," in Proc. 6th Int. Conf. Soft Comput., Japan, 2000, ISBN: 4-938717-04-2.
29. M. Last, A. Kandel, and O. Maimon, "Information-theoretic algorithm for feature selection," Pattern Recognit. Lett., vol. 22, pp. 799-811, 2001.
30. M. Leshno, V. A. Lin, A. Pinkus, and S. Schocken, "Multilayered feedforward networks with a nonpolynomial activation function can approximate any function," Neural Netw., vol. 6, pp. 861-867, 1993.
31. Y. Lin and G. A. Cunningham, III, "A new approach to fuzzy-neural system modeling," IEEE Trans. Fuzzy Syst., vol. 3, no. 2, pp. 190-198, May 1995.
32. H. Liu and L. Yu, "Towards integrating feature selection algorithms for classification and clustering," IEEE Trans. Knowl. Data Eng., vol. 17, no. 4, pp. 491-502, May 2005.
33. D. J. C. MacKay, "Bayesian interpolation," Neural Comput., vol. 4, no. 3, pp. 415-447, 1992.
34. D. J. C. MacKay, "A practical Bayesian framework for backprop networks," Neural Comput., vol. 4, no. 3, pp. 448-472, 1992.
35. D. J. C. MacKay, "The evidence framework applied to classification networks," Neural Comput., vol. 4, no. 5, pp. 698-714, 1992.
36. R. M. Neal, "Bayesian learning for neural networks," in Lecture Notes in Statistics. Berlin, Germany: Springer-Verlag, vol. 118.
37. J. Novovicova, P. Pudil, and J. Kittler, "Divergence based feature selection for multimodal class densities," IEEE Trans. Pattern Anal. Mach. Intell., vol. 18, no. 2, pp. 218-223, 1996.
38. N. R. Pal, "Soft computing for feature analysis," Fuzzy Sets Syst. vol. 103, pp. 201-221, 1999.
39. 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.
40. J. Park and I. W. Sandberg, "Universal approximation using radial basis function networks," Neural Comput., vol. 3, pp. 246-257, 1991.
41. M. L. Raymer, W. F. Punch, E. D. Goodman, L. A. Kuhn, and A. K. Jain, "Dimensionality reduction using genetic algorithms," IEEE Trans. Evol. Comput., vol. 4, no. 2, pp. 164-171, Apr. 2000.
42. M. R. Rezaee, B. Goedhart, B. P. F. Lelieveldt, and J. H. C. Reiber, "Fuzzy feature selection," Pattern Recognit., vol. 32, pp. 2011-2019, 1999.
43. D. W. Ruck, S. K. Rogers, and M. Kabrisky, "Feature selection using a multilayered perceptron," J. Neural Netw. Comput., pp. 40-48, 1990.
44. R. Setino, "Neural network feature selector," IEEE Trans. Neural Netw., vol. 8, no. 3, pp. 654-662, May 1997.
45. J. M. Steppe, Jr., "Integrated feature and architecture selection," IEEE Trans. Neural Netw., vol. 7, no. 4, pp. 1007-1014, Jul. 1996.
46. P. Sykacek, "On input selection with reversible jump Markov chain Monte Carlo sampling," in Advances in Neural Information Processing Systems, S. A. Sola, T. K. Leen, and K. R. Müller, Eds. Cambridge, MA: MIT Press, 2000, vol. 12, pp. 638-644.
47. X. Yao, "Evolving artificial neural networks," Proc. IEEE, vol. 87, no. 9, pp. 1423-1447, Sep. 1999.
48. M. Sugeno and T. Yasukawa, "A fuzzy-logic based approach to qualitative modeling," IEEE Trans. Fuzzy Syst., vol. 1, no. 1, pp. 7-31, Feb. 1993.
49. J. M. Zurada, A. Malinowski, and S. Usui, "Perturbation method for detecting redundant inputs of perceptron networks," Neurocomputing vol. 14, pp. 177-193, 1997.