Combining multiple class distribution modified subsamples in a single tree

Authors:
Jesús M. Pérez;Javier Muguerza;Olatz Arbelaitz;Ibai Gurrutxaga;José I. Martín
Affiliations:
Department of Computer Architecture and Technology, University of the Basque Country, M. Lardizabal, 1, 20018 Donostia, Spain1http://www.sc.ehu.es/aldapa.1;Department of Computer Architecture and Technology, University of the Basque Country, M. Lardizabal, 1, 20018 Donostia, Spain1http://www.sc.ehu.es/aldapa.1;Department of Computer Architecture and Technology, University of the Basque Country, M. Lardizabal, 1, 20018 Donostia, Spain1http://www.sc.ehu.es/aldapa.1;Department of Computer Architecture and Technology, University of the Basque Country, M. Lardizabal, 1, 20018 Donostia, Spain1http://www.sc.ehu.es/aldapa.1;Department of Computer Architecture and Technology, University of the Basque Country, M. Lardizabal, 1, 20018 Donostia, Spain1http://www.sc.ehu.es/aldapa.1
Venue:
Pattern Recognition Letters
Year:
2007

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Abstract

This work describes the Consolidated Tree Construction (CTC) algorithm: a single tree is built based on a set of subsamples. This way the explaining capacity of the classifier is not lost even if many subsamples are used. We show how CTC algorithm can use undersampling to change class distribution without loss of information, building more accurate classifiers than C4.5.