Classification rule extraction based on the rough concept lattice

• Published in: Kybernetes Vol. 39; no. 8; pp. 1336 - 1343
• Main Authors: Haifeng, Yang, Jifu, Zhang, Lihua, Hu
• Format: Journal Article
• Language: English
• Published: London Emerald Group Publishing Limited 10.08.2010
• Subjects: Algorithms; Approximation; Classification; Cybernetics; Data analysis; Data collection; Data mining; Data processing; Extraction; Fuzzy sets; Lattices; Mathematical analysis; Set theory; Studies; Uncertainty; Data collection; Data analysis; Cybernetics; Classification
• ISSN: 0368-492X; 1758-7883
• DOI: 10.1108/03684921011063637

Purpose - The purpose of this paper is to examine the important application value of extending the concept of classification rule, so that it can describe and measure the uncertainty of classification knowledge.Design methodology approach - The rough concept lattice (RCL), which is an effective tool for uncertain data analysis and knowledge discovery, reflects a kind of unification of concept intent and upper lower approximation extent, as well as the certain and uncertain relations between objects and attributes.Findings - A classification rules extraction algorithm, extraction algorithm of classification rule (EACR), based on the RCL is presented by adapting the rough degree to measure uncertainty of classification rule. The algorithm EACR is experimentally validated by taking the star spectrum data as the decision context.Practical implications - An efficient way for classification rule extraction is provided.Originality value - The algorithm EACR based on the RCL is presented by adapting the rough degree to measure uncertainty of classification rule.