A first study on decomposition strategies with data with class noise using decision trees

Authors:
José A. Sáez;Mikel Galar;Julián Luengo;Francisco Herrera
Affiliations:
Department of Computer Science and Artificial Intelligence, University of Granada, CITIC-UGR, Granada, Spain;Department of Automática y Computación, Universidad Pública de Navarra, Pamplona, Spain;Department of Civil Engineering, LSI, University of Burgos, Burgos, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, CITIC-UGR, Granada, Spain
Venue:
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Year:
2012

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Abstract

Noise is a common problem that produces negative consequences in classification problems. When a problem has more than two classes, that is, a multi-class problem, an interesting approach to deal with noise is to decompose the problem into several binary subproblems, reducing the complexity and consequently dividing the effects caused by noise into each of these subproblems. This contribution analyzes the use of decomposition strategies, and more specifically the One-vs-One scheme, to deal with multi-class datasets with class noise. In order to accomplish this, the performance of the decision trees built by C4.5, with and without decomposition, are studied. The results obtained show that the use of the One-vs-One strategy significantly improves the performance of C4.5 when dealing with noisy data.