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\iteman{io-port 06089866}
\itemau{S\'aez, Jos\'e A.; Galar, Mikel; Luengo, Juli\'an; Herrera, Francisco}
\itemti{A first study on decomposition strategies with data with class noise using decision trees.}
\itemso{Corchado, Emilio (ed.) et al., Hybrid artificial intelligent systems. 7th international conference, HAIS 2012, Salamanca, Spain, March 28--30, 2012. Proceedings, Part II. Berlin: Springer (ISBN 978-3-642-28930-9/pbk). Lecture Notes in Computer Science 7209. Lecture Notes in Artificial Intelligence, 25-35 (2012).}
\itemab
Summary: 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.
\itemrv{~}
\itemcc{}
\itemut{noisy data; class noise; one-vs-one; decomposition strategies; ensembles; classification}
\itemli{doi:10.1007/978-3-642-28931-6\_3}
\end