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Complexity
Volume 2017 (2017), Article ID 9023970, 17 pages
https://doi.org/10.1155/2017/9023970
Research Article

A New Robust Classifier on Noise Domains: Bagging of Credal C4.5 Trees

Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

Correspondence should be addressed to Joaquín Abellán; se.rgu.iasced@nallebaj

Received 9 June 2017; Revised 10 October 2017; Accepted 2 November 2017; Published 3 December 2017

Academic Editor: Roberto Natella

Copyright © 2017 Joaquín Abellán et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The knowledge extraction from data with noise or outliers is a complex problem in the data mining area. Normally, it is not easy to eliminate those problematic instances. To obtain information from this type of data, robust classifiers are the best option to use. One of them is the application of bagging scheme on weak single classifiers. The Credal C4.5 (CC4.5) model is a new classification tree procedure based on the classical C4.5 algorithm and imprecise probabilities. It represents a type of the so-called credal trees. It has been proven that CC4.5 is more robust to noise than C4.5 method and even than other previous credal tree models. In this paper, the performance of the CC4.5 model in bagging schemes on noisy domains is shown. An experimental study on data sets with added noise is carried out in order to compare results where bagging schemes are applied on credal trees and C4.5 procedure. As a benchmark point, the known Random Forest (RF) classification method is also used. It will be shown that the bagging ensemble using pruned credal trees outperforms the successful bagging C4.5 and RF when data sets with medium-to-high noise level are classified.