A hybrid feature selection method based on fuzzy feature selection and consistency measures

In This paper, we present a new method for dealing with feature subset selection based on fuzzy methods and consistency measures for handling classification problems. In fuzzy classifier systems the classification is obtained by a number of fuzzy If-Then rules including linguistic terms such as Low and High that fuzzify each feature. First, we project the original data set into a fuzzy space, then we select the feature subset based on the consistency measures. The proposed method which is an integration of fuzzy feature subset selection and consistency measures can select relevant features to get higher average classification accuracy rates than each of the above mentioned methods. The applicability of the proposed method has been demonstrated by reducing the number of features used for the classification of nine real-world data sets.

[1]  Settimo Termini,et al.  A Definition of a Nonprobabilistic Entropy in the Setting of Fuzzy Sets Theory , 1972, Inf. Control..

[2]  A. Bonaert.  Introduction to the theory of Fuzzy subsets , 1977, Proceedings of the IEEE.

[3]  Keinosuke Fukunaga,et al.  A Branch and Bound Algorithm for Feature Subset Selection , 1977, IEEE Transactions on Computers.

[4]  Sankar K. Pal,et al.  Fuzzy Set Theoretic Approach: A Tool for Speech and Image Recognition , 1982 .

[5]  Josef Kittler,et al.  Pattern recognition : a statistical approach , 1982 .

[6]  Basabi Chakraborty,et al.  Fuzzy Set Theoretic Measure for Automatic Feature Evaluation , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  Paul W. Baim.  A Method for Attribute Selection in Inductive Learning Systems , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Jack Sklansky,et al.  A note on genetic algorithms for large-scale feature selection , 1989, Pattern Recognit. Lett..

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

[10]  Hisao Ishibuchi,et al.  Adaptive fuzzy rule-based classification systems , 1996, IEEE Trans. Fuzzy Syst..

[11]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[12]  Pat Langley,et al.  Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..

[13]  John F. Hurdle.  The synthesis of compact fuzzy neural circuits , 1997, IEEE Trans. Fuzzy Syst..

[14]  Andrzej Skowron,et al.  Rough-Fuzzy Hybridization: A New Trend in Decision Making , 1999 .

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

[16]  Huan Liu,et al.  Discretization: An Enabling Technique , 2002, Data Mining and Knowledge Discovery.

[17]  Blaz Zupan,et al.  Orange: From Experimental Machine Learning to Interactive Data Mining , 2004, PKDD.

[18]  José Manuel Benítez,et al.  Consistency measures for feature selection , 2008, Journal of Intelligent Information Systems.