Chunk incremental learning for cost-sensitive hinge loss support vector machine

Highlights：

• We proposed a chunk incremental learning algorithm for CSHL-SVM (i.e., CICSHL-SVM) that can update a trained model without re-training from scratch when incorporating a chunk of new samples.

• Our method is efficient because it can update the trained model not only for one sample at a time but also for multiple samples at a time.

• The experimental results on a variety of datasets not only confirm the effectiveness of CSHL-SVM but also show that our method is more efficient than the batch algorithm of CSHL-SVM and the single incremental algorithm.