EasyEnsemble. M for Multiclass Imbalance Problem
Qian Li · 2014
The potential useful information in the majority class is ignored by stochastic under-sampling.When under-sampling is applied to multi-class imbalance problem,this situation becomes even worse.In this paper,EasyEnsemble.M for multi-class imbalance problem is proposed.The potential useful information contained in the majority classes which is ignored is explored by stochastic sampling the majority classes for multiple times.Then,sub-classifiers are learned and a strong classifier is obtained by using hybrid ensemble techniques.Experimental results show that EasyEnsemble.M is superior to other frequently used multi-class imbalance learning methods when G-mean is used as performance measure.