A New Classifier Design with Fuzzy Functions

This paper presents a new fuzzy classifier design, which constructs one classifier for each fuzzy partition of a given system. The new approach, namely Fuzzy Classifier Functions (FCF), is an adaptation of our generic design on Fuzzy Functions to classification problems. This approach couples any fuzzy clustering algorithm with any classification method, in a unique way. The presented model derives fuzzy functions (rules) from data to classify patterns into number of classes. Fuzzy c-means clustering is used to capture hidden fuzzy patterns and a linear or a non-linear classifier function is used to build one classifier model for each pattern identified. The performance of each classifier is enhanced by using corresponding membership values of the data vectors as additional input variables. FCF is proposed as an alternate representation and reasoning schema to fuzzy rule base classifiers. The proposed method is evaluated by the comparison of experiments with the standard classifier methods using cross validation on test patterns.

[1]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[2]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[3]  Shigeo Abe,et al.  A fuzzy classifier with ellipsoidal regions , 1997, IEEE Trans. Fuzzy Syst..

[4]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

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

[6]  Harris Drucker,et al.  Support vector machines for spam categorization , 1999, IEEE Trans. Neural Networks.

[7]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[8]  Magne Setnes,et al.  Fuzzy relational classifier trained by fuzzy clustering , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[9]  Carlos Soares,et al.  A Comparison of Ranking Methods for Classification Algorithm Selection , 2000, ECML.

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

[11]  Vojislav Kecman,et al.  Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models , 2001 .

[12]  Enric Plaza,et al.  Machine Learning: ECML 2000 , 2003, Lecture Notes in Computer Science.

[13]  John H. Lilly,et al.  Evolutionary design of a fuzzy classifier from data , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[14]  Lipo Wang,et al.  Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing) , 2005 .

[15]  Lipo Wang.  Support vector machines : theory and applications , 2005 .

[16]  A Hybrid Bayesian Optimal Classifier Based on Neuro-fuzzy Logic , 2006, ICNC.

[17]  Jin-Kao Hao,et al.  A Hybrid GA/SVM Approach for Gene Selection and Classification of Microarray Data , 2006, EvoWorkshops.

[18]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[19]  I. Turksen,et al.  Comparison of Fuzzy Functions with Fuzzy Rule Base Approaches , 2006 .

[20]  Ramazan Aktas,et al.  PREDICTING FINANCIAL FAILURE OF THE TURKISH BANKS , 2006 .

[21]  Mei-Ling Huang,et al.  Erratum to: Glaucoma detection using adaptive neuro-fuzzy inference system [Expert Systems with Applications 32 (2) (2007) 458-468] , 2007, Expert Syst. Appl..

[22]  I. Burhan Türksen,et al.  Fuzzy functions with support vector machines , 2007, Inf. Sci..

[23]  Mei-Ling Huang,et al.  Glaucoma detection using adaptive neuro-fuzzy inference system , 2007, Expert Syst. Appl..

[24]  Hsuan-Tien Lin,et al.  A note on Platt’s probabilistic outputs for support vector machines , 2007, Machine Learning.

[25]  I. Burhan Türksen,et al.  Fuzzy functions with LSE , 2008, Appl. Soft Comput..