An incremental cost-sensitive support vector machine

Cost-sensitive learning is an important field in machine learning, which widely exists in real-world applications, such as cancer diagnosis, credit application, etc. Cost-sensitive support vector machine proposed by Masnadi et al. handles cost-sensitive problems through making the hinge loss function cost-sensitive, which has better generalization accuracy than other traditional cost-sensitive algorithms. In practice data are obtained one batch after another. Conventional batch algorithms would waste a lot of time when appending samples, because they should re-train the model from scratch. To make the cost-sensitive support vector machine more practical in on-line learning problems, an incremental cost-sensitive support vector machine algorithm was proposed, which can directly update the trained model without re-training it from scratch when appending samples. Experiment study on several datasets show that our algorithm is significantly more efficient than batch algorithms of the cost-sensitive support vector machine.