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

Mahua Bhattacharya, Arpita Das

Abstract


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.

Keywords


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

Full Text:

PDF

References


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.
http://dx.doi.org/10.1109/34.31448

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.
http://dx.doi.org/10.1109/icec.1994.350006

[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.
http://dx.doi.org/10.1109/TEVC.2003.819263

J. Yang and V. Hanovar, "Feature subset selection using genetic algorithm", Journal of IEEE Intelligent Systems, vol. 13, pp. 44-49, 1998.
http://dx.doi.org/10.1109/5254.671091

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.
http://dx.doi.org/10.2333/bhmk.26.145

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.
http://dx.doi.org/10.1007/3-540-44732-6_19

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.
http://dx.doi.org/10.1016/0165-0114(94)90018-3

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.




DOI: https://doi.org/10.15837/ijccc.2010.4.2495



Copyright (c) 2017 Mahua Bhattacharya, Arpita Das

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

CC-BY-NC  License for Website User

Articles published in IJCCC user license are protected by copyright.

Users can access, download, copy, translate the IJCCC articles for non-commercial purposes provided that users, but cannot redistribute, display or adapt:

  • Cite the article using an appropriate bibliographic citation: author(s), article title, journal, volume, issue, page numbers, year of publication, DOI, and the link to the definitive published version on IJCCC website;
  • Maintain the integrity of the IJCCC article;
  • Retain the copyright notices and links to these terms and conditions so it is clear to other users what can and what cannot be done with the  article;
  • Ensure that, for any content in the IJCCC article that is identified as belonging to a third party, any re-use complies with the copyright policies of that third party;
  • Any translations must prominently display the statement: "This is an unofficial translation of an article that appeared in IJCCC. Agora University  has not endorsed this translation."

This is a non commercial license where the use of published articles for commercial purposes is forbiden. 

Commercial purposes include: 

  • Copying or downloading IJCCC articles, or linking to such postings, for further redistribution, sale or licensing, for a fee;
  • Copying, downloading or posting by a site or service that incorporates advertising with such content;
  • The inclusion or incorporation of article content in other works or services (other than normal quotations with an appropriate citation) that is then available for sale or licensing, for a fee;
  • Use of IJCCC articles or article content (other than normal quotations with appropriate citation) by for-profit organizations for promotional purposes, whether for a fee or otherwise;
  • Use for the purposes of monetary reward by means of sale, resale, license, loan, transfer or other form of commercial exploitation;

    The licensor cannot revoke these freedoms as long as you follow the license terms.

[End of CC-BY-NC  License for Website User]


INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL (IJCCC), With Emphasis on the Integration of Three Technologies (C & C & C),  ISSN 1841-9836.

IJCCC was founded in 2006,  at Agora University, by  Ioan DZITAC (Editor-in-Chief),  Florin Gheorghe FILIP (Editor-in-Chief), and  Misu-Jan MANOLESCU (Managing Editor).

Ethics: This journal is a member of, and subscribes to the principles of, the Committee on Publication Ethics (COPE).

Ioan  DZITAC (Editor-in-Chief) at COPE European Seminar, Bruxelles, 2015:

IJCCC is covered/indexed/abstracted in Science Citation Index Expanded (since vol.1(S),  2006); JCR2018: IF=1.585..

IJCCC is indexed in Scopus from 2008 (CiteScore2018 = 1.56):

Nomination by Elsevier for Journal Excellence Award Romania 2015 (SNIP2014 = 1.029): Elsevier/ Scopus

IJCCC was nominated by Elsevier for Journal Excellence Award - "Scopus Awards Romania 2015" (SNIP2014 = 1.029).

IJCCC is in Top 3 of 157 Romanian journals indexed by Scopus (in all fields) and No.1 in Computer Science field by Elsevier/ Scopus.

 

 Impact Factor in JCR2018 (Clarivate Analytics/SCI Expanded/ISI Web of Science): IF=1.585 (Q3). Scopus: CiteScore2018=1.56 (Q2); Editors-in-Chief: Ioan DZITAC & Florin Gheorghe FILIP.