Classification of Multi-class Imbalanced Data: Data Difficulty Factors and Selected Methods for Improving Classifiers

The multiple class imbalanced problem is still less investigated than its binary counterpart. In particular, the sources of its difficulties have not been sufficiently studied so far. Therefore, in this paper we summarize the few literature works on the difficulty factors and present our own latest research results. The binary method for an identification of the types of minority examples is generalized for multiple imbalance classes. The second part of this paper presents three our recent methods for learning classifies from multi-class imbalanced data which exploit information on the aforementioned difficulty factors.

Automated summary

This paper summarizes the difficulty factors and research status of multiple class imbalanced problem, and presents three new methods for learning classifiers from multi-class imbalanced data.