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Proceedings Paper

A flexible self-learning model based on granular computing
Author(s): Ting Wei; Yu Wu; Yinguo Li
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Paper Abstract

Granular Computing(GrC) is an emerging theory which simulates the process of human brain understanding and solving problems. Rough set theory is a tool for dealing with uncertainty and vagueness aspects of knowledge model. SMLGrC algorithm introduces GrC to classical rough set algorithms, and makes the length of the rules relatively short but it can not process mass data sets. In order to solve this problem, based on the analysis of the hierarchical granular model of information table, the method of Granular Distribution List(GDL) is introduced to generate granule, and a granular computing algorithm(SLMGrC) is improved. Sample Covered Factor(SCF) is also introduced to control the generation of rules when the algorithm generates conflicting rules. The improved algorithm can process mass data sets directly without influencing the validity of SLMGrC. Experiments demonstrated the validity and flexibility of our method.

Paper Details

Date Published: 9 April 2007
PDF: 8 pages
Proc. SPIE 6570, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2007, 65700M (9 April 2007); doi: 10.1117/12.718325
Show Author Affiliations
Ting Wei, Chongqing Univ. of Posts and Telecommunications (China)
Yu Wu, Chongqing Univ. of Posts and Telecommunications (China)
Yinguo Li, Chongqing Univ. of Posts and Telecommunications (China)


Published in SPIE Proceedings Vol. 6570:
Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2007
Belur V. Dasarathy, Editor(s)

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