A Combination Method for Multi-class Imbalanced Data Classification

Abstract

Multi-class imbalanced data classification problem is common in the real world, but traditional binary classification methods cannot be directly applied. Existing solutions include designing new multi-class classification algorithm and dividing multi-class classification problem into binary classification problem. The latter includes two widely used strategies, namely one versus all (OVA) and one versus one (OVO). In this paper, we propose a combination method based on all and one (A&O), which is a combination of OVA and OVO, for multi-class imbalanced data classification problem. The method is developed by combining A&O and data balancing technique named SMOTE. Comparative experiments on 13 UCI datasets show that the proposed method performs well.