Combining feature selection and hybrid approach redefinition in handling class imbalance and overlapping for multi-class imbalanced

<span>In the classification process that contains class imbalance problems. In addition to uneven distribution of instances which causes poor performance, overlapping problems also cause performance degradation. This paper proposes a method combining feature selection and hybrid approach redefinition (HAR) in handling for multi-class imbalanced. HAR was ensembles problem. The main contribution this work is produce new can overcome problem must be able give better results terms classifier overlap degrees achieved by improving an ensemble learning algorithm preprocessing technique <span>using minimizing under SMOTE (MOSS). MOSS known as very popular overlapping. To validate accuracy proposed method, research use augmented R-Value, Mean AUC, F-Measure, G-Mean, Precision. model evaluated against (MBP+CGE) It found superior when subjected indicate with precision.</span></span>