Title: Local rotation-based ensemble

Authors: S.B. Kotsiantis

Addresses: Educational Software Development Laboratory, Department of Mathematics, University of Patras, University Campus, 26504, Rio, Patras, Greece

Abstract: Many data analysis problems involve an investigation of relationships between attributes in heterogeneous databases, where different prediction models can be more appropriate for different regions. We propose a technique of local rotation-based ensemble of weak classifiers. In order to determine rotation forests, we identify local regions having similar characteristics and then build local classification experts on each of these regions describing the relationship between the data characteristics and the target class. We performed a comparison with other well-known combining methods using weak classifiers as based learners, on standard benchmark datasets and we took better accuracy.

Keywords: data mining; supervised machine learning; classification; local ensemble; rotation-based ensemble; weak classifiers; rotation forests; local regions; data characteristics; target class.

DOI: 10.1504/IJKEDM.2010.034841

International Journal of Knowledge Engineering and Data Mining, 2010 Vol.1 No.2, pp.147 - 160

Available online: 24 Aug 2010 *

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