An Algorithm for Selective Preprocessing of Multi-class Imbalanced Data

In this paper we propose a new algorithm called SPIDER3 for selective preprocessing of multi-class imbalanced data sets. While it borrows selected ideas (i.e., combination of relabeling and local resampling) from its predecessor - SPIDER2, it introduces several important extensions. Unlike SPIDER2, it is able to handle directly multi-class problems. Moreover, it considers the relevance of specific decision classes to control the order of their processing. Finally, it uses information about relations between specific classes (modeled with misclassification costs) to better control the extent of changes introduced locally to preprocessed data. We performed a computational experiment on artificial 3-class data sets to evaluate and compare SPIDER3 to SPIDER2 with temporarily aggregated classes and the results confirmed advantages of the new algorithm.