Incremental Cost-Sensitive Support Vector Machine With Linear-Exponential Loss

作者：Yue Ma, Kun Zhao, Qi Wang, Yingjie Tian · 发表于：IEEE Access · 年份：2020 · DOI：10.1109/access.2020.3015954 · 被引用次数：29 · 研究领域：Imbalanced Data Classification Techniques、Electricity Theft Detection Techniques、Text and Document Classification Technologies

Incremental learning or online learning as a branch of machine learning has attracted more attention recently. For large-scale problems and dynamic data problem, incremental learning overwhelms batch learning, because of its efficient treatment for new data. However, class imbalance problem, which always appears in online classification brings a considerable challenge for incremental learning. The serious class imbalance problem may directly lead to a useless learning system. Cost-sensitive learning is an important learning paradigm for class imbalance problems and widely used in many applications. In this article, we propose an incremental cost-sensitive learning method to tackle the class imbalance problems in the online situation. This proposed algorithm is based on a novel cost-sensitive support vector machine, which uses the Linear-exponential (LINEX) loss to implement high cost for minority class and low cost for majority class. Using the half-quadratic optimization, we first put forward the algorithm for the cost-sensitive support vector machine, called CSLINEX-SVM*. Then we propose the incremental cost-sensitive algorithm, ICSL-SVM. The results of numeric experiments demonstrate that the proposed incremental algorithm outperforms some conventional batch algorithms except the proposed CSLINEX-SVM*.