An Effective Ensemble Method for Multi-class Classification and Regression for Imbalanced Data

Abstract

In the field of Data Mining, classification and regression plays a vital role as they are useful in various real-life domains. Most of the real-life data suffer from data imbalance problem. The performances of the standard algorithms are hindered for the data imbalance problem. A number of methods have been introduced for imbalance data classification. However, most of them are designed for binary class imbalance problems. Furthermore, they suffer from various problems like loss of useful information, likelihood of overfitting, unexpected mistakes etc. On the other hand, data imbalance problem exists in regression analysis also, although very few existing methods consider this problem. Hence, we propose an effective recursive based ensemble method for multi-class imbalance data classification. We also extend our method to propose an effective recursive based method for solving the data imbalance problem in regression. Extensive performance analyses show that our proposed approach achieves high performance in multi-class classification on class imbalance data and regression analysis on skewed or imbalance data. The experimental results also show that our method outperforms various existing methods for imbalance classification and regression.