Pseudo-label neighborhood rough set: Measures and attribute reductions

The scale of the radius for constructing neighborhood relation has a great effect on the results of neighborhood rough sets and corresponding measures. A very small radius frequently brings us nothing because any two different samples are separated from each other, though these two samples have the same label. If the radius is growing, then there is a serious risk that samples with different labels may fall into the same neighborhood. Obviously, the radius based neighborhood relation does not take the labels of samples into account, which will lead to unsatisfactory discrimination. To fill such gap, a pseudo-label strategy is systematically studied in rough set theory. Firstly, a pseudo-label neighborhood relation is proposed. Such relation can differentiate samples by not only the distance but also the pseudo labels of samples. Therefore, both the neighborhood rough set and some corresponding measures can be re-defined. Secondly, attribute reductions are explored based on the re-defined measures. The heuristic algorithm is also designed to compute reducts. Finally, the experimental results over UCI data sets tell us that our pseudo-label strategy is superior to the traditional neighborhood approach. This is mainly because the former can significantly reduce the uncertainties and improve the classification accuracies. The Wilcoxon signed rank test results also show that neighborhood approach and pseudo-label neighborhood approach are so different from the viewpoints of the measures and attribute reductions in rough set theory. (C) 2018 Elsevier Inc. All rights reserved.