Fuzzy One-Class Classification Model Using Contamination Neighborhoods | Zendy

Lev V. Utkin | Zendy

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Fuzzy One-Class Classification Model Using Contamination Neighborhoods

Author(s) -

Lev V. Utkin

Publication year - 2012

Publication title -

advances in fuzzy systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.38

H-Index - 19

eISSN - 1687-711X

pISSN - 1687-7101

DOI - 10.1155/2012/984325

Subject(s) - fuzzy logic , data mining , support vector machine , computer science , fuzzy classification , class (philosophy) , artificial intelligence , fuzzy set , data set , set (abstract data type) , pattern recognition (psychology) , machine learning , programming language

A fuzzy classification model is studied in the paper. It is based on the contaminated (robust) model which produces fuzzy expected risk measures characterizing classification errors. Optimal classification parameters of the models are derived by minimizing the fuzzy expected risk. It is shown that an algorithm for computing the classification parameters is reduced to a set of standard support vector machine tasks with weighted data points. Experimental results with synthetic data illustrate the proposed fuzzy model

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