Asymmetric classifier based on kernel PLS for imbalanced data

In classification tasks, class imbalance problem has been reported to hinder the performance of some standard classifiers, such as nearest neighbors algorithm. This paper presents an improvement to kernel partial least squares classifier (KPLSC) is proposed to deal with the class imbalance problem. This improvement is applicable to all cases no matter whether the data sets are linearly separable or not. Experiments on datasets from different domains show that the improvement performs well in classification problems.