CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Article:A Study on the Analysis of Genetic Algorithms with Various Classification Techniques for Feature Selection

by Mrs.E.P.Ephzibah, Mrs. B. Sarojini, Mrs. J.Emerald Sheela
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 8 - Number 8
Year of Publication: 2010
Authors: Mrs.E.P.Ephzibah, Mrs. B. Sarojini, Mrs. J.Emerald Sheela
10.5120/1226-1784

Mrs.E.P.Ephzibah, Mrs. B. Sarojini, Mrs. J.Emerald Sheela . Article:A Study on the Analysis of Genetic Algorithms with Various Classification Techniques for Feature Selection. International Journal of Computer Applications. 8, 8 ( October 2010), 33-37. DOI=10.5120/1226-1784

@article{ 10.5120/1226-1784,
author = { Mrs.E.P.Ephzibah, Mrs. B. Sarojini, Mrs. J.Emerald Sheela },
title = { Article:A Study on the Analysis of Genetic Algorithms with Various Classification Techniques for Feature Selection },
journal = { International Journal of Computer Applications },
issue_date = { October 2010 },
volume = { 8 },
number = { 8 },
month = { October },
year = { 2010 },
issn = { 0975-8887 },
pages = { 33-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume8/number8/1226-1784/ },
doi = { 10.5120/1226-1784 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:56:55.686651+05:30
%A Mrs.E.P.Ephzibah
%A Mrs. B. Sarojini
%A Mrs. J.Emerald Sheela
%T Article:A Study on the Analysis of Genetic Algorithms with Various Classification Techniques for Feature Selection
%J International Journal of Computer Applications
%@ 0975-8887
%V 8
%N 8
%P 33-37
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the world of curse of dimensionality feature selection plays a very important role in reducing the entire feature collection with the limited subset of features. Reducing the number of features pave way for various advantaged as well as simplifies the task. Feature selection means finding the suitable set of features which will contribute the most of it to the solution with minimal or null error rate. Selected features are to be tested with the help of classifiers, so that the subset of selected features can be proved to be optimal when compared to other features subsets individually as well as a group. Genetic algorithms are now days play a vital role among any other methodology in selecting the features based on the Theory of Evolution and on the “Survival of the fitness”. It is a heuristic approach. To cooperate with the GA approach we have the classifiers which will go hand in hand to bring out the final set of features along with their prediction accuracy. In this paper I have analyzed four of the classifiers and compared them with their performance and the unit of accuracy.

References
  1. Bengio Y., 2000. Gradient-based Optimization of Hyperparameters. Neural Computation, 12(8), pp. 1889-1900.
  2. Bi J., Bennett K.P., Embrechts M., Breneman C.M., Song M., 2003. Dimensionality reduction via sparse support vector machines. J. Mach. Learn. Res. 3, pp. 1229-1243.
  3. CHEN Pengwai, WNAG Jungying, LEE Hahnming “ Model selection of SVMs with GA Approach” Proc 2004 IEEE Int joint conf on Neural.
  4. Cortes C., Vapnik V.N., 1995. Support vector networks. Mach. Learn. 20(3), pp. 273-297.
  5. Dasarathy.B.V, Nearest Neighbor (NN) Norms: NN Pattern classification Techniques, IEEE Computer society press, Los Alamitos California (1990)
  6. Guyon I., Weston J., Barnhill S., Bapnik V., 2002. Gene selection for cancer Classification using support vector machines. Machine Learning, 46(1-3), pp. 389-422.
  7. Jacek Jarmulak and Susan craw, “Genetic algorithms for Feature Selection and Weighting” Proc of the IJCAI ’99 Workshop on Automating the construction of case based reasoners.
  8. Jensen.R, Q. Shen, Tolerance-based and fuzzy-rough feature selection, in: Proceedings of the 16th International Conference on Fuzzy Systems (FUZZ- IEEE'07), 2007, pp. 877–882.
  9. K. Kira, L.A. Rendell, The feature selection problem: traditional methods and a new algorithm, in: Proceedings of Ninth National Conference on Artificial Intelligence, 1992, pp. 129–134.
  10. Klir, G.J. and B.yuan (1995) fuzzy sets and fuzzy logic theory and applications, Prentice Hall, Englewood cliffs, NJ, USA.
  11. Metin. N., Heang-Ping, C., and Berhan, S., Optimal Neural Network Architecture Selection: Improvement in Computerized Detection of Micro calcifications, Acad Radiol 9, 2002, pp.420-429.
  12. O. Maimon, A. Kandel, and M. Last, Information-Theoretic Fuzzy Approach to Knowledge Discovery in Databases. In Advances in Soft Computing - Engineering Design and Manufacturing, R. Roy, T. Furuhashi and P.K. Chawdhry, Eds. Springer- Verlag, London, 1999.
  13. Raymer M.L., PunchW.F., 2000. Goodman E.D., Kuhn L.A., Jain A.K. Dimensionality reduction using genetic algorithms. IEEE Trans. Evol. Comput., 4(2), pp. 164-171
  14. Ruck D.W. S.K.Rogers and M.Kabrisky(1990) Feature Selection using a multilayer perceptron”, Journal of Neural Network Computing. 40-48
  15. Sikora R., Piramuthu S., 2007. Framework for efficient feature selection in genetic algorithm based data mining. European Journal of Operational Research, 180(2), pp. 723-737.
  16. Swiniarsk.R.W.i, A. Skowron, Rough set methods in feature selection and recognition, Pattern Recognition Lett. 24 (6) (2003) 833–849.
  17. Verma, B. K., A neural network based technique to locate and classify micro calcifications in digital mammograms, IEEE World Congress on Computational Intelligence, WCCI’98, Anchorage, USA, 1998, pp. 2163-2168.
  18. Weston J., Mukherjee S., Chapelle O., Ponntil M., Poggio T., Vapnik V., 2001. Feature selection for SVMs. Advances in Neural Information Processing Systems, MIT Press, Cambridge, MA.
  19. Zhong.N, J. Dong, S. Ohsuga, Using rough sets with heuristics for feature selection, J. Intell. Inf. Syst. 16 (3) (2001) 199–214.
  20. Zilberstein.S Using Anytime Algorithms in Intelligent Systems, AI Magazine, vol. 17, no. 3, pp. 73-83, 1996
Index Terms

Computer Science
Information Sciences

Keywords

Feature Selection Genetic Algorithm SVM KNN Fuzzy Rough Set Neural Networks classification

Powered by PhDFocusTM