A Segmented Local Offset Method for Imbalanced Data Classification Using Quasi-Linear Support Vector Machine
Peifeng Liang, Xin Yuan, Weite Li, Jinglu Hu · 2018
Within-class imbalance problems often occur in imbalance classification which worsen the imbalance distribution problem and increase the learning concept complexity. However, most of existing methods for imbalanced classification focus on rectifying the between-class which are insufficiencies and inappropriateness in many different scenarios. This paper proposes a novel quasi-linear SVM with local offset adjustment method for imbalance classification problem. Our chief aim is to use leaning offsets of sub-clusters obtained according to imbalance ratios of sub-clusters to adjust classifier to achieve the best results. For this purpose, firstly, a geometry-based partitions method for imbalance dataset is introduced to partition the input space into several linearly separable partitions so as to construct a quasi-linear kernel and obtain an SVM classifier. Then a local offset method based on F-score value for linearly separable imbalance dataset is introduced to obtain leaning offset of each partition. At last the quasi-linear SVM with local offset adjustment is used to get the classifier for imbalance datasets. Simulation results on different real different real world datasets show that the proposed method is effective for imbalanced data classifications.