Local averaging of heterogeneous regression models

Article type: Research Article

Authors: Kotsiantis, S.B.

Affiliations: Educational Software Development Laboratory, Department of Mathematics, P.A. Box: 1399, University of Patras, Patra 26500, Greece. Tel.: +30 2610 997833/+30 2610 997313; Fax: +30 2610 997313; E-mail: [email protected]

Abstract: Many learning problems involve an exploration of relationships between features in heterogeneous datasets, where different learning models can be more suitable for different regions. We propose herein a technique of localized averaging of regression models. This technique identifies local regions which have similar characteristics and then uses the average value of local experts to describe the relationship between the predictive feature values and the target value. We performed a comparison with other famous combining methods on standard benchmark datasets, and the correlation coefficient of the proposed method was higher.

Keywords: Machine learning, data mining, regression, ensembles of regression models

DOI: 10.3233/HIS-2006-3203

Journal: International Journal of Hybrid Intelligent Systems, vol. 3, no. 2, pp. 99-107, 2006