Li Yi, Zhang Benwen, Mo Hongming, Hu Jiancheng, Liu Yuncheng, Tan Xingqiang
Institute of Computer Application Research, Sichuan Minzu College, Kangding 626001, China.
College of Applied Mathematics, Chengdu University of Information Technology, Chengdu 610225, China.
iScience. 2024 Oct 29;27(12):111270. doi: 10.1016/j.isci.2024.111270. eCollection 2024 Dec 20.
Neighborhood rough set (NRS) have been successfully applied to attribute reduction (AR). However, most current methods of AR based on NRS are supervised or semi-supervised. This limits their ability to process data without decision information. When granulating data samples, NRS considers only the number of samples within the neighborhood radius. It does not consider distribution information between samples, which can result in the loss of original data information. To address the aforementioned issue, we propose an unsupervised attribute reduction (UAR) strategy based on variable precision weighted neighborhood dependency (VPWND) (UAR_VPWND). We compare algorithm UAR_VPWND to existing classical UAR algorithms using public datasets. The experimental results show that algorithm UAR_VPWND can select fewer attributes to maintain or improve the performance of clustering learning algorithms.
