A Novel Feature Selection Method for Classification Using a Fuzzy Criterion

Although many classification methods take advantage of fuzzy sets theory, the same cannot be said for feature reduction methods. In this paper we explore ideas related to the use of fuzzy sets and we propose a novel fuzzy feature selection method tailored for the Regularized Generalized Eigenvalue Classifier ReGEC. The method provides small and robust subsets of features that can be used for supervised classification. We show, using real world datasets that the performance of ReGEC classifier on the selected features well compares with that obtained using them all.

[1]  Panos M. Pardalos,et al.  A classification method based on generalized eigenvalue problems , 2007, Optim. Methods Softw..

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

[3]  David G. Stork,et al.  Pattern Classification , 1973 .

[4]  Nikhil R. Pal,et al.  An Integrated Mechanism for Feature Selection and Fuzzy Rule Extraction for Classification , 2012, IEEE Transactions on Fuzzy Systems.

[5]  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.

[6]  Madhu Chetty,et al.  Differential prioritization in feature selection and classifier aggregation for multiclass microarray datasets , 2006, Data Mining and Knowledge Discovery.

[7]  Terrence J. Sejnowski,et al.  Analysis of hidden units in a layered network trained to classify sonar targets , 1988, Neural Networks.

[8]  Uzay Kaymak,et al.  Fuzzy criteria for feature selection , 2012, Fuzzy Sets Syst..

[9]  K.Z. Mao,et al.  Orthogonal forward selection and backward elimination algorithms for feature subset selection , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Lipo Wang,et al.  Data dimensionality reduction with application to simplifying RBF network structure and improving classification performance , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[11]  Bernhard Schölkopf,et al.  Use of the Zero-Norm with Linear Models and Kernel Methods , 2003, J. Mach. Learn. Res..

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

[13]  P. Pardalos,et al.  Classification and Characterization of Gene Expression Data with Generalized Eigenvalues , 2009 .

[14]  Mario Rosario Guarracino,et al.  Multiclass Generalized Eigenvalue Proximal Support Vector Machines , 2010, 2010 International Conference on Complex, Intelligent and Software Intensive Systems.

[15]  Joseph Aguilar-Martin,et al.  Sensor placement and fault detection using an efficient fuzzy feature selection approach , 2010, 49th IEEE Conference on Decision and Control (CDC).

[16]  Witold Pedrycz,et al.  Feature analysis through information granulation and fuzzy sets , 2002, Pattern Recognit..

[17]  Boudewijn P. F. Lelieveldt,et al.  Fuzzy feature selection , 1999, Pattern Recognit..

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

[19]  Ioannis B. Theocharis,et al.  SVM-FuzCoC: A novel SVM-based feature selection method using a fuzzy complementary criterion , 2010, Pattern Recognit..

[20]  Yun Li,et al.  Fuzzy feature selection based on min-max learning rule and extension matrix , 2008, Pattern Recognit..

[21]  Igor Kononenko,et al.  Estimating Attributes: Analysis and Extensions of RELIEF , 1994, ECML.

[22]  Aixia Guo,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2014 .