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

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*.