A HYBRID FEATURE SELECTION METHOD BASED ON FISHER SCORE AND GENETIC ALGORITHM

Fisher score and genetic algorithm are widely used for feature selection. However, some redundant features will be selected by Fisher score, and the convergence properties may be worse if the initial population of genetic algorithm is generated by a random manner. To improve the performance of feature selection by Fisher score and genetic algorithm, we propose a hybrid feature selection method, which merging the advantages of Fisher score and genetic algorithm together. It aims at utilizing the features' Fisher score to generate the initial population of genetic algorithm. To begin with, the Fisher scores of all the features will be mapped into a specific interval by a linear function, and then the rescaled Fisher scores will be utilized to generate the initial population of genetic algorithm. Finally, the initial population will be used in the subsequent procedure of genetic algorithm to perform feature selection with elitist strategy for reference. In this paper, we choose four data sets of Sonar, WDBC, Arrhythmia, and Hepatitis to test the performance of our algorithm. Feature subsets of the four data sets will be selected by our algorithm, and then the dimensionality of data sets will be reduced according to the selected feature subsets, respectively. 1-NN classifier is used to classify the dimensionality reduced data sets, and respectively, achieving the classification

[1]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[2]  Daphne Koller,et al.  Toward Optimal Feature Selection , 1996, ICML.

[3]  Marko Robnik-Sikonja,et al.  Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.

[4]  Mineichi Kudo,et al.  Comparison of algorithms that select features for pattern classifiers , 2000, Pattern Recognit..

[5]  Josef Kittler,et al.  Fast branch & bound algorithms for optimal feature selection , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[7]  Bir Bhanu,et al.  Genetic algorithm based feature selection for target detection in SAR images , 2003, Image Vis. Comput..

[8]  Deng Cai,et al.  Laplacian Score for Feature Selection , 2005, NIPS.

[9]  Rich Caruana,et al.  Greedy Attribute Selection , 1994, ICML.

[10]  Roberto Battiti,et al.  Using mutual information for selecting features in supervised neural net learning , 1994, IEEE Trans. Neural Networks.

[11]  Yiming Yang,et al.  A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.

[12]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[13]  Feiping Nie,et al.  Trace Ratio Criterion for Feature Selection , 2008, AAAI.

[14]  Thomas Bäck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..

[15]  Jihoon Yang,et al.  Feature Subset Selection Using a Genetic Algorithm , 1998, IEEE Intell. Syst..

[16]  Steven Guan,et al.  Feature selection for modular GA-based classification , 2004, Appl. Soft Comput..

[17]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[18]  Anil K. Jain,et al.  Dimensionality reduction using genetic algorithms , 2000, IEEE Trans. Evol. Comput..

[19]  Le Song,et al.  Supervised feature selection via dependence estimation , 2007, ICML '07.