FORF-S: A Novel Classification Technique for Class Imbalance Problem

Abstract

In recent years, the class imbalance problem that aims to correctly classify imbalanced data sets and improve the classification performance of minority instances has received attention. Such problem can be roughly described as one of the class(es) termed as minority class(es) contains much smaller instances than the others, also referred as majority class(es). To address this problem, a novel classification method called focused online random forest based on synthetic minority oversampling technique (FORF-SMOTE) is proposed in this paper and simply expressed as FORF-S, which constructs two online random forests respectively trained by original training dataset and new generated dataset, then further jointly constitute the model. Instead of oversampling the minority instances in data level, the algorithm in this paper is motivated by making the sampling strategies integrated into algorithm level to create classifiers, which can better identify the minority class. Moreover, the method is also compared with other state-of-the-art methods and the results have demonstrated that the proposed algorithm takes advantages of the aforementioned methods.

Keywords

• Imbalanced data
• online random forest
• synthetic minority oversampling technique
• nearest neighbor