Adaptive kernel scaling support vector machine with application to a prostate cancer image study

Abstract

The support vector machine (SVM) is a popularly used classifier in applications such as pattern recognition, texture mining and image retrieval owing to its flexibility and interpretability. However, its performance deteriorates when the response classes are imbalanced. To enhance the performance of the support vector machine classifier in the imbalanced cases we investigate a new two stage method by adaptively scaling the kernel function. Based on the information obtained from the standard SVM in the first stage, we conformally rescale the kernel function in a data adaptive fashion in the second stage so that the separation between two classes can be effectively enlarged with incorporation of observation imbalance. The proposed method takes into account the location of the support vectors in the feature space, therefore is especially appealing when the response classes are imbalanced. The resulting algorithm can efficiently improve the classification accuracy, which is confirmed by intensive numerical studies as well as a real prostate cancer imaging data application.

Keywords:

• Classification
• data-adaptive kernel
• imaging data
• imbalanced data
• separating hyperplane
• support vector machine

Acknowledgments

This work has been supported by a grant from the Natural Sciences and Engineering Research Council of Canada (NSERC) and a team grant from the Canadian Institute of Health Research. Liu's research is partially supported by the Fundamental Funds for Central Universities.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work has been supported by a grant from the Natural Sciences and Engineering Research Council of Canada (NSERC), a team grant from the Canadian Institute of Health Research, and the SHUFE-2018110185 Startup Fund of Shanghai University of Finance and Economics