期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2016
卷号：9
期号：8
页码：333-346
出版社：SERSC
摘要：Numerous studies have focused on feature selection using many algorithms, but most of these algorithms encounter problems when the amount of data is large. In this paper, we propose an algorithm that handles a large amount of data by partitioning the data to process areduction, and then selecting the intersection of all reducts as a stable reduct. This algorithm is successful but may suffer from loss of information if the samples are unsuitable. The proposed algorithm is based on discernibility matrix and function. Furthermore, the method can address the case in which the data consist of a significant amount of information. Our results showthat theproposed algorithm is powerful and flexible enough to successfully target a range of different domains and can effectively reduce computational complexity as well as increase reduction efficiency. The efficiency of Proposed Algorithm is illustrated by experiments with UCI datasets further.
关键词：Rough set algorithm; Minimal attributes reduction; Partition algorithm; ;Red;uct and Core
