Density-based multi-weight vector support vector machine

Abstract

Recently, proposed multi-weight vector support vector machine (MVSVM) considers all of points and views them as equally important points. In real cases, most of the points of a dataset are highly correlated, at least locally, or the dataset has an inherent geometrical property. These points generally lie in the high density regions and are crucial for data classification. This motivates the rush toward new classifiers that can sufficiently take advantage of the points in the high density regions. In this paper, a novel binary classifier called density-based multi-weight vector support vector machine (DMVSVM) is presented. With the introduction of underlying correlated information between points, DMVSVM not only inherits the merit of MVSVM, but also has its additional characteristics: 1) density weighting method is adopted to measure the importance of points in the same class; 2) having comparable or better classification ability compared to MVSVM. The experimental results on publicly available datasets confirm the effectiveness of our method.