Cost-sensitive meta-learning classifiers: MEPAR-miner and DIFACONN-miner

Cost-sensitive learning which deals with classification problems that have non-uniform costs has attracted great attention from the machine learning and data mining communities in recent years. In this study, a rescaling based meta-learning scheme is applied to cost-insensitive MEPAR-miner and DIFACONN-miner algorithms which were previously developed by the authors in order to make the algorithms cost-sensitive. Rescaling is realized in two ways by means of oversampling and undersampling by resampling the training instances in proportion to their costs. The proposed algorithms can extract rules for both binary and n-ary classification problems and also handle data sets that have missing values. An extensive computational study is performed on different types of classification benchmark problems with the aim of testing the performances of the algorithms. Comparisons with traditional cost-sensitive meta-learning algorithms and cost-insensitive MEPAR-miner and DIFACONN-miner algorithms show that the proposed cost-sensitive algorithms are competitive meta-learning algorithms and able to produce accurate and effective classification rules with low misclassification costs.