Granular Computing-based Binary Discernibility Matrix Attribute Reduction Algorithm

Granular computing (GrC) fuses theoretical research results such as rough sets, fuzzy sets, word computing, quotient space and interval computing. GrC is a new hotspot in artificial intelligence and information processing. Layer viewpoint is a concept in GrC and multi-layer GrC has great significance in the problem solving and knowledge structure of human beings. This paper introduced the layer viewpoint into the binary discernibility matrix and proposed a granular computing-based binary discernibility matrix attribute reduction algorithm. Through a concrete engineering example, it can verify that GrC-based binary discernibility matrix attribute reduction algorithm is effective in minimum attribute reduction.