A density-based maximum margin machine classifier

Classic support vector machine classifiers find separating hyperplanes by considering patterns of data sets, such as so-called support vectors without any character, i.e., without any global information concerning the relationship between one point and other points. In this study, we propose a density-based maximum margin machine classifier based on the idea of replacing support vectors with edge-points. Each edge-point of a data set is characterized by a density that represents the distance between the point and its neighbours. In some sense, the density character of a pattern (edge-point) is used here as global information relation the pattern to other points. To evaluate the performance of the proposed approach, we test it on several benchmark data sets. A comparative study demonstrates the advantages of our new approach.