Genetic Algorithm Based Feature Selection In a Recognition Scheme Using Adaptive Neuro Fuzzy Techniques


  • Mahua Bhattacharya Indian Institute of Information Technology & Management Morena Link Road, Gwalior-474003, India
  • Arpita Das Institute of Radio Physics & Electronics University of Calcutta 92, A.P.C. Road, Kolkata-700009


Adaptive neuro- fuzzy, Genetic algorithm, Feature selection, pattern recognition


The problem of feature selection consists of finding a significant feature subset of input training as well as test patterns that enable to describe all information required to classify a particular pattern. In present paper we focus in this particular problem which plays a key role in machine learning problems. In fact, before building a model for feature selection, our goal is to identify and to reject the features that degrade the classification performance of a classifier. This is especially true when the available input feature space is very large, and need exists to develop an efficient searching algorithm to combine these features spaces to a few significant one which are capable to represent that particular class. Presently, authors have described two approaches for combining the large feature spaces to efficient numbers using Genetic Algorithm and Fuzzy Clustering techniques. Finally the classification of patterns has been achieved using adaptive neuro-fuzzy techniques. The aim of entire work is to implement the recognition scheme for classification of tumor lesions appearing in human brain as space occupying lesions identified by CT and MR images. A part of the work has been presented in this paper. The proposed model indicates a promising direction for adaptation in a changing environment.


I. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley, 1989.

B. K. Fukunaga and R. R. Hayes, "Effects of sample size in classifier design," IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, pp. 873-885, Aug. 1989.

D'haeseleer, P. "Context preserving crossover in genetic programming"' Proc. of the 1994 IEEE World Congress on Computational Intelligence, vol. 1, pages 256-261, Orlando, FL, USA. IEEE Press, 1994.

[4]. Burke, E., Gustafson, S., and Kendall, G. Diversity in genetic programming: An analysis of measures and correlation with fitness. IEEE Transactions on Evolutionary Computation, 8(1): pp. 47-62, 2004.

J. Yang and V. Hanovar, "Feature subset selection using genetic algorithm", Journal of IEEE Intelligent Systems, vol. 13, pp. 44-49, 1998.

S. S. Sanz, G.C. Valls, F. P. Cruz, J. S. Sanchis, C. B. Calzn, "Enhancing Genetic Feature Selection Through Restricted Search and Walsh Analysis", IEEE Trans. on Systems, Man, and Cybernetics, Vol. 34, No. 4, November 2004.

P. Leray and P. Gallinari, "Feature selection with neural networks," Behaviormetrika, vol. 26, Jan. 1999.

B. Hassibi and D. G. Stork, "Second order derivatives for network pruning: optimal brain surgeon," in Advances in Neural information Processing Systems, S. J. Hanson, J. D. Cowan, and C. L. Giles, Eds. San Mateo, CA: Morgan Kaufmann, 1993, vol. 5, pp. 164-171.

L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees, 3rd ed. London, U.K.: Chapman & Hall, 1984.

T. E. Campos, I. Bloch, and R. M. Cesar Jr., "Feature selection based on fuzzy distances between clusters: first results on simulated data," Lecture Notes in Computer Science, vol. 20, no.13, pp. 186, 2001.

E. Nabil; A. Badr; I. Farag; "An Immuno-Genetic Hybrid Algorithm", International Journal of Computers, Communications & Control, vol. IV, no. 4, ISSN 1841 - 9836; E-ISSN 1841-9844, 2009.

Adlassnig, K. P., "Fuzzy neural network learning model for image recognition." Integrated Computer-Aided Engineering, pp. 43-55, 1982.

Kim, J.S. and H. S. Cho, "A fuzzy logic and neural network approach to boundary detection for noisy images." Fuzzy Sets and Systems, pp. 141-159, 1994.

Jang, J.-S.R., C.-T. Sun, E. Mizutani, "Neuro-Fuzzy and Soft Computing, A Computational Approach to Learning and Machine Intelligent" Pearson Education.

C. Mu´noz, F. Vargas, J. Bustos, M. Curilem, S. Salvo ; H. Miranda; "Fuzzy Logic in Genetic Regulatory Network Models", International Journal of Computers, Communications & Control, vol. IV, no. 4, ISSN 1841 - 9836; E-ISSN 1841 - 9844, 2009.



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