Binary classification of imbalanced datasets: The case of CoIL challenge 2000.

• The prediction task of CoIL challenge 2000 is addressed in the paper. • Three different methods are proposed for direct mailing problem of CoIL challenge 2000. • The proposed methods outperform the method proposed by the winner of the challenge. • The proposed methods overcome, also, the unbalanced dataset issue of the problem. This paper presents some approaches based on data mining techniques to solve the prediction task of Computational Intelligence and Learning (CoIL) Challenge 2000. The prediction task of the contest is a direct mailing problem and the goal is to improve its response rate. The main issue in this competition is the incompatibility of the dataset in which the distribution of the classes of the target attribute is highly unbalanced. This in turn causes high error rate in identifying the minority class samples. Three different level methods including data-level, algorithm-level, and hybrid method are used to overcome this issue. The specificity, sensitivity, precision-recall, and ROC criteria are employed to compare the performance of the methods. Among the methods proposed in this paper, the best one performs much better than the winner of the competition. [ABSTRACT FROM AUTHOR]