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.

DI GESU', V., & LO BOSCO, G. (2007). Combining one class fuzzy KNN’s. In F. Masulli, S. Mitra, & G. Pasi (a cura di), Applications of Fuzzy Sets Theory (pp. 152-160) [10.1007/978-3-540-73400-0_19].

Combining one class fuzzy KNN’s

DI GESU', Vito;LO BOSCO, Giosue'
2007

Abstract

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.
Settore INF/01 - Informatica
978-3-540-73399-7
978-3-540-73400-0
DI GESU', V., & LO BOSCO, G. (2007). Combining one class fuzzy KNN’s. In F. Masulli, S. Mitra, & G. Pasi (a cura di), Applications of Fuzzy Sets Theory (pp. 152-160) [10.1007/978-3-540-73400-0_19].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/16670
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