Granular support vector machines for medical binary classification problems

Abstract

We propose a new learning model called granular support vector machines for data classification problems. Granular support vector machines systematically and formally combines the principles from statistical learning theory and granular computing theory. It works by building a sequence of information granules and then building a support vector machine in each information granule. In this paper, we also give a simple but efficient implementation method for modeling a granular support vector machine by building just two information granules in the top-down way (that is, halving the whole feature space). The hyperplane used to halve the feature space is selected by extending statistical margin maximization principle. The experiment results on three medical binary classification problems show that finding the splitting hyperplane is not a trivial task. For some datasets and some kernel functions, granular support vector machines with two information granules could achieve some improvement on testing accuracy, but for some other datasets, building one single support vector machine in the whole feature space gets a little better performance. How to get the optimal information granules is still an open problem. The important issue is that granular support vector machines proposed in This work provides an interesting new mechanism to address complex classification problems, which are common in medical or biological information processing applications.