Call for Paper - September 2020 Edition
IJCA solicits original research papers for the September 2020 Edition. Last date of manuscript submission is August 20, 2020. Read More

Cost Effective Approach on Feature Selection using Genetic Algorithms and LS-SVM Classifier

Evolutionary Computation for Optimization Techniques
© 2010 by IJCA Journal
Number 1 - Article 3
Year of Publication: 2010

E.P.Ephzibah. Cost Effective Approach on Feature Selection using Genetic Algorithms and LS-SVM Classifier. IJCA Special Issue on Evolutionary Computation (1):16–20, 2010. Full text available. BibTeX

	author = {E.P.Ephzibah},
	title = {Cost Effective Approach on Feature Selection using Genetic Algorithms and LS-SVM Classifier},
	journal = {IJCA Special Issue on Evolutionary Computation},
	year = {2010},
	number = {1},
	pages = {16--20},
	note = {Full text available}


This work focuses on the problem of diagnosing the disease in the earlier stage by applying a selection technique based on genetic algorithm and least square support vector machines. The implementation of the technique analyses the accuracy of the classifier as well as the cost effectiveness in the implementation. This technique will help us to diagnose the disease with a limited number of tests that could be performed with minimal amount. We use evolutionary computation which is a subfield of artificial intelligence or computational intelligence that involves combinatorial optimization problems. Evolutionary computation uses iterative progress, such as growth or development in a population. This population is then selected in a guided random search using parallel processing to achieve the desired end. Such processes are often inspired by biological mechanisms of evolution. The obtained results using the genetic algorithms approach show that the proposed method is able to find an appropriate feature subset and SVM classifier achieves better results than other methods.


  • Baresel.A: Automating structural tests using evolutionary algorithms,(German) Diploma Theses, Humboldt_University of Berlin, Germany, 2000.
  • C. J. C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery, vol. 2, pp.121-167, 1998
  • A.L.Blum and R.L Rivest, “Training a three node Neural Networks is NP-Complete”, Neural Networks, vol. 5 , pp.117-127, 1992.
  • R. Caruana and D. Freitag, “Greedy Attribute Selection”, Proc. 11th Int’l Conf. Machine Learning, pp. 28-36, 1994.
  • S. Das, “Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection,” Proc. 18th Int’l Conf. Machine Learning, pp. 74-81, 2001.
  • M.Dash, K. Choi, P.Scheuermann and H.Liu, “Feature selection for clustering – a Filter Solution “, Proc. Second Int’l Conference. Data mining, pp.115-122, 2002.
  • J.G. Dy and C.E. Brodley, “Feature Subset Selection and Order Identification for Unsupervised Learning,” Proc. 17th Int’l Conf. Machine Learning, pp. 247-254, 2000.
  • M.A. Hall, “Correlation-Based Feature Selection for Discrete and Numeric Class Machine Learning,” Proc. 17th Int’l Conf. Machine Learning, pp. 359-366, 2000.
  • Y. Kim, W. Street, and F. Menczer, “Feature Selection for Unsupervised Learning via Evolutionary Search,” Proc. Sixth ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining, pp. 365-369, 2000.
  • R. Kohavi and G.H. John, “Wrappers for Feature Subset Selection,” Artificial Intelligence, vol. 97, nos. 1-2, pp. 273-324, 1997.
  • H. Liu and R. Setiono, “A Probabilistic Approach to Feature Selection-A Filter Solution,” Proc. 13th Int’l Conf. Machine Learning, pp. 319-327, 1996.
  • Murphy P M, Aha Irvine D W. CA: University of California, Department of Information and Computer Science[EB/OL].,1994.
  • A.Y. Ng, “On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training Examples,” Proc. 15th Int’l Conf. Machine Learning, pp. 404-412, 1998.
  • P.J.M van Laarhoven and E.H.L.Aarts :Simulated Annealing Theory and applications , (Netherlands : Kluwer Academic Pub-1992),PP 9-10.
  • Wegener.J. Sthamer, H, Baresel,A (2001): Evolutionary Test Environment for Automatic Structural Testing. Special Issue of Information and Software Technology, vol 43, pp. 851 – 854, 2001.
  • Wegener.J, Grochtmann ,M: Verifying Timing Constraints of Real-Time Systems by Means of Evolutionary Testing. Real-Time Systems, 15, pp. 275-298, 1998.
  • E. Xing, M. Jordan, and R. Karp, “Feature Selection for High-Dimensional Genomic Microarray Data,” Proc. 15th Int’l Conf. Machine Learning, pp. 601-608, 2001.
  • L. Yu and H. Liu, “Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution,” Proc. 20th Int’l Con Machine Learning, pp. 856-863, 2003.