Feature Selection for Support Vector Machines Base on Modified Artificial Fish Swarm Algorithm

Feature selection is a search process to find the optimal feature subset to describe the characteristics of dataset exactly. Artificial Fish Swarm Algorithm is a novel meta-heuristic search algorithm, which can solve the problem of optimization by simulate the behaviors of fish swarm. This study proposes a modified version of Artificial Fish Swarm Algorithm to select the optimal feature subset to improve the classification accuracy for Support Vector Machines. The empirical results showed that the performance of the proposed method was superior to that of basic version of Artificial Fish Swarm Algorithm.

[1]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[2]  Tao Liu,et al.  Feature Optimization Based on Artificial Fish-Swarm Algorithm in Intrusion Detections , 2009, 2009 International Conference on Networks Security, Wireless Communications and Trusted Computing.

[3]  Cheng-Lung Huang,et al.  A GA-based feature selection and parameters optimizationfor support vector machines , 2006, Expert Syst. Appl..

[4]  Christopher R. Houck,et al.  A Genetic Algorithm for Function Optimization: A Matlab Implementation , 2001 .

[5]  Nello Cristianini,et al.  Support vector machine classification and validation of cancer tissue samples using microarray expression data , 2000, Bioinform..

[6]  Saeed Farzi.  Efficient Job Scheduling in Grid Computing with Modified Artificial Fish Swarm Algorithm , 2009 .

[7]  Guoqiang Peter Zhang,et al.  Neural networks for classification: a survey , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[8]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[9]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[10]  Hiroshi Motoda,et al.  Feature Selection for Knowledge Discovery and Data Mining , 1998, The Springer International Series in Engineering and Computer Science.

[11]  Shuzong Wang,et al.  A Hybrid of Artificial Fish Swarm Algorithm and Particle Swarm Optimization for Feedforward Neural Network Training , 2007 .

[12]  Li Xiao,et al.  An Optimizing Method Based on Autonomous Animats: Fish-swarm Algorithm , 2002 .

[13]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.