Accelerator for supervised neighborhood based attribute reduction

Abstract In neighborhood rough set, radius is a key factor. Different radii may generate different neighborhood relations for discriminating samples. Unfortunately, it is possible that two samples with different labels are regarded as indistinguishable, mainly because the neighborhood relation does not always provide satisfactory discriminating performance. Moreover, it should be noticed that the process of obtaining reducts in terms of multiple different radii is very time-consuming, mainly because different radii imply different reducts and those reducts should be searched, respectively. To solve the above problems, not only a supervised neighborhood relation is proposed for obtaining better discriminating performance, but also an accelerator is designed to speed up the process of obtaining reducts. Firstly, both intra-class radius and inter-class radius are proposed to distinguish samples. Different from the previous approaches, the labels of samples are taken into account and then this is why our approach is referred to as the supervised neighborhood based strategy. Secondly, from the viewpoint of the variation of radius, an accelerator is designed which aims to quickly obtain multiple radii based reducts. Such mechanism is based on the consideration that the reduct in terms of the previous radius may guide the process of obtaining the reduct in terms of the current radius. The experimental results over 12 UCI data sets show the following: 1) compared with the traditional and pseudo-label neighborhood based reducts, our supervised neighborhood based reducts can provide higher classification accuracies; 2) our accelerator can significantly reduce the elapsed time for obtaining reducts. This study suggests new trends for considering neighborhood rough set related topics.