Incremental Cost-sensitive Support Vector Machine with Linear-exponential Loss

• Published in: IEEE access 2020-01, Vol.8, p.1-1
• Main Authors: Ma, Yue, Zhao, Kun, Wang, Qi, Tian, Yingjie
• Format: Article
• Language: English
• Subjects: Algorithms; class imbalance; Computational modeling; cost-sensitive; Distance learning; Fasteners; incremental learning; Learning systems; LINEX loss; Machine learning; Machine learning algorithms; Online learning; Optimization; support vector machine; Support vector machines
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3015954

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 paper, 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*.