A flexible self-learning model based on granular computing

Ting Wei; Yu Wu; Yinguo Li

doi:10.1117/12.718325

9 April 2007 A flexible self-learning model based on granular computing

Ting Wei, Yu Wu, Yinguo Li

Proceedings Volume 6570, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2007; 65700M (2007) https://doi.org/10.1117/12.718325
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

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.

Citation Download Citation

Ting Wei, Yu Wu, and Yinguo Li "A flexible self-learning model based on granular computing", Proc. SPIE 6570, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2007, 65700M (9 April 2007); https://doi.org/10.1117/12.718325

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