Description

ABSTRACT

Classification in imbalanced domains is a recent challenge in data mining. It refer to

imbalanced classification when data presents many examples from one class and few from

the other class, and the less representative class is the one which has more interest from the

point of view of the learning task. One of the most used techniques to tackle this problem

consists in preprocessing the data previously to the learning process. This preprocessing

could be done through under-sampling; removing examples, mainly belonging to the

majority class; and over-sampling, by means of replicating or generating new minority

examples. In this paper, we propose an under-sampling procedure guided by evolutionary

algorithms to perform a training set selection for enhancing the decision trees obtained by the

C4.5 algorithm and the rule sets obtained by PART rule induction algorithm. The proposal

has been compared with other under-sampling and over-sampling techniques and the results

indicate that the new approach is very competitive in terms of accuracy when comparing

with over-sampling and it outperforms standard under-sampling. Moreover, the obtained

models are smaller in terms of number of leaves or rules generated and they can consider

more interpretabl.