Combining One Class Fuzzy KNN's

This paper introduces a parallel combination of N> 2 one class fuzzy KNN(FKNN) classifiers. The classifier combination consists of a new optimization procedure based on a genetic algorithm applied to FKNN's, that differ in the kind of similarity used. We tested the integration techniques in the case of N= 5 similarities that have been recently introduced to face with categorical data sets. The assessment of the method has been carried out on two public data set, the Masquerading User Data (www.schonlau.net) and the badges database on the UCI Machine Learning Repository (http://www.ics.uci.edu/~mlearn/). Preliminary results show the better performance obtained by the fuzzy integration respect to the crisp one.

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