Attribute reduction in multi-source decision systems

Abstract

Data processing for information from different sources is a hot research topic in the contemporary data. Attribute reduction methods of multi-source decision systems (MSDS) are proposed in this paper. Firstly, based on the integrity of original effective information preservation, a consistent attribute reduction of the multi-source decision system is proposed. Secondly, in the case of a certain loss of original effective information, data is compressed by the fusion of conditional entropy. Then attribute reduction preserving knowledge unchanged are studied in the decision system obtained by fusion. Accordingly, examples are introduced to further elaborate the theory proposed in this paper.