Semantic-based Genetic Algorithm for Feature Selection

In this paper, an optimal feature selection method considering sematic of features, which is preprocess of document classification is proposed. The feature selection is very important part on classification, which is composed of removing redundant features and selecting essential features. LSA (Latent Semantic Analysis) for considering meaning of the features is adopted. However, a supervised LSA which is suitable method for classification problems is used because the basic LSA is not specialized for feature selection. We also apply GA (Genetic Algorithm) to the features, which are obtained from supervised LSA to select better feature subset. Finally, we project documents onto new selected feature subset and classify them using specific classifier, SVM (Support Vector Machine). It is expected to get high performance and efficiency of classification by selecting optimal feature subset using the proposed hybrid method of supervised LSA and GA. Its efficiency is proved throu gh experiments using internet news classification with low features.

[1]  Pasi Luukka,et al.  Feature selection using fuzzy entropy measures with similarity classifier , 2011, Expert Syst. Appl..

[2]  Wei-Ying Ma,et al.  Supervised latent semantic indexing for document categorization , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[3]  Chih-Ming Chen,et al.  Two novel feature selection approaches for web page classification , 2009, Expert Syst. Appl..

[4]  Houkuan Huang,et al.  Feature selection for text classification with Naïve Bayes , 2009, Expert Syst. Appl..

[5]  T. Landauer,et al.  A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. , 1997 .

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

[7]  Huan Liu,et al.  Toward integrating feature selection algorithms for classification and clustering , 2005, IEEE Transactions on Knowledge and Data Engineering.

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

[9]  Brian D. Davison,et al.  Web page classification: Features and algorithms , 2009, CSUR.

[11]  Marko Grobelnik,et al.  Feature selection using linear classifier weights: interaction with classification models , 2004, SIGIR '04.

[12]  Zhang Jie.  An Evaluation of Feature Selection Methods for Text Categorization , 2005 .

[13]  Edward R. Dougherty,et al.  Performance of feature-selection methods in the classification of high-dimension data , 2009, Pattern Recognit..

[14]  R. Sabourin,et al.  Feature subset selection using genetic algorithms for handwritten digit recognition , 2001, Proceedings XIV Brazilian Symposium on Computer Graphics and Image Processing.

[15]  Richard A. Harshman,et al.  Indexing by latent semantic indexing analysis , 1990 .

[16]  T. Landauer,et al.  Indexing by Latent Semantic Analysis , 1990 .

[17]  Leslie S. Smith,et al.  Feature subset selection in large dimensionality domains , 2010, Pattern Recognit..

[18]  Sutanu Chakraborti,et al.  Sprinkling: Supervised Latent Semantic Indexing , 2006, ECIR.

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

[20]  Pedro Larrañaga,et al.  Feature Subset Selection by Bayesian network-based optimization , 2000, Artif. Intell..

[21]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[22]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Ron Kohavi,et al.  Irrelevant Features and the Subset Selection Problem , 1994, ICML.

[24]  Ali Selamat,et al.  Web page feature selection and classification using neural networks , 2004, Inf. Sci..

[25]  Anirban Dasgupta,et al.  Feature selection methods for text classification , 2007, KDD '07.