A New Fuzzy Supervised Classification Method Based on Aggregation Operator

A new fuzzy supervised classification method based on aggregation operator is proposed in the present article. The proposed classifier aggregates the information extracted by exploring feature-wise degree of belonging to classes. I uses a pi-type membership function and MEAN (average) aggregation reasoning rule (operator). The effectiveness of the proposed classifier is verified with four benchmark data sets including a realtime financial domain data. Various performance measures are used for quantitative evaluation of the classifier. Experimental results on these data sets illustrate significant improvement in the classification performance of the proposed method compared to three other fuzzy classifiers, namely, explicit fuzzy, fuzzy k-nearest neighbor and fuzzy maximum likelihood.

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

[2]  Andrew B. Watson,et al.  Digital images and human vision , 1993 .

[3]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[4]  Lorenzo Bruzzone,et al.  Neuro-Fuzzy Fusion: A New Approach to Multiple Classifier System , 2006, 9th International Conference on Information Technology (ICIT'06).

[5]  Witold Pedrycz,et al.  Fuzzy sets in pattern recognition: Methodology and methods , 1990, Pattern Recognit..

[6]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[7]  Ludmila I. Kuncheva,et al.  Fuzzy Classifier Design , 2000, Studies in Fuzziness and Soft Computing.

[8]  Farid Melgani,et al.  An explicit fuzzy supervised classification method for multispectral remote sensing images , 2000, IEEE Trans. Geosci. Remote. Sens..

[9]  H. Ishibuchi,et al.  Distributed representation of fuzzy rules and its application to pattern classification , 1992 .

[10]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[11]  Didier Dubois,et al.  On the use of aggregation operations in information fusion processes , 2004, Fuzzy Sets Syst..

[12]  Fangju Wang,et al.  Fuzzy supervised classification of remote sensing images , 1990 .

[13]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[14]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[15]  Shigeo Abe,et al.  A method for fuzzy rules extraction directly from numerical data and its application to pattern classification , 1995, IEEE Trans. Fuzzy Syst..

[16]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[17]  Donald R. Tveter.  The pattern recognition basis of artificial intelligence , 1998 .

[18]  James M. Keller,et al.  A fuzzy K-nearest neighbor algorithm , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[19]  Ivan Popchev,et al.  Properties of the aggregation operators related with fuzzy relations , 2003, Fuzzy Sets Syst..

[20]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .