In Machine Learning, ensemble methods have been receiving a great deal of attention. Techniques such as Bagging and Boosting have been successfully applied to a variety of problems. Nevertheless, such techniques are still susceptible to the effects of noise and outliers in the training data. We propose a new method for the generation of pools of classifiers based on Bagging, in which the probability of an instance being selected during the resampling process is inversely proportional to its instance hardness, which can be understood as the likelihood of an instance being misclassified, regardless of the choice of classifier. The goal of the proposed method is to remove noisy data without sacrificing the hard instances which are likely to be found on class boundaries. We evaluate the performance of the method in nineteen public data sets, and compare it to the performance of the Bagging and Random Subspace algorithms. Our experiments show that in high noise scenarios the accuracy of our method is significantly better than that of Bagging.

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The results presented in this paper have been developed as part of a collaborative project between SiDi and the Centre of Informatics at the Federal University of Pernambuco (CIn/UFPE), financed by Samsung Eletronica da Amazônia Ltda., under the auspices of the Brazilian Federal Law of Informatics no. 8248/91. The first and third authors were also partially supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).