Imbalanced Data Classification Based on Feature Selection Techniques

The difficulty of the many classification tasks lies in the analyzed data nature, as disproportionate number of examples from different class in a learning set. Ignoring this characteristics causes that canonical classifiers display strongly biased performance on imbalanced datasets. In this work a novel classifier ensemble forming technique for imbalanced datasets is presented. On the one hand it takes into consideration selected features used for training individual classifiers, on the other hand it ensures an appropriate diversity of a classifier ensemble. The proposed method was tested on the basis of the computer experiments carried out on the several benchmark datasets. Their results seem to confirm the usefulness of the proposed concept.