Local voting of weak classifiers

Article type: Research Article

Authors: Kotsiantis, S.^{; *} | Pintelas, P.

Affiliations: Educational Software Development Laboratory, Department of Mathematics, University of Patras, P.A. Box: 1399, University of Patras, Patra 26500, Greece

Correspondence: [*] Corresponding author. Tel.: +30 2610 997833; +30 2610 992965; +30 2610 997313; Fax: +30 2610 992965; E-mail: [email protected]

Abstract: Many data mining problems involve an investigation of relationships between features in heterogeneous datasets, where different learning algorithms can be more appropriate for different regions. We propose herein a technique of localized voting of weak classifiers. This technique identifies local regions which have similar characteristics and then uses the votes of each local expert to describe the relationship between the data characteristics and the target class. We performed a comparison with other well known combining methods on standard benchmark datasets and the accuracy of the proposed method was greater.

Keywords: Machine learning, data mining, classification, ensembles of classifiers

DOI: 10.3233/KES-2005-9308

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 9, no. 3, pp. 239-248, 2005