A two-stage rule base optimization method based on combination of classical algorithm and GA for intelligent decision support system

DOI

10.2991/iske.2007.126How to use a DOI?

Keywords

rule base optimization; rule base maintenance; intelligent decision support system; genetic algorithm; generative knowledge system

Abstract

Intelligent decision support system performance depends on knowledge base quality. With the decision problem and knowledge base becoming more complicated, it is necessary to provide effective method to optimize the management of knowledge base. Based on detailed analyzing of running characteristics and potential detects of rule base in IDSS, a novel two-stage optimization method is proposed. Conventional optimization approach and genetic algorithm are combined to recognize and eliminate kinds of defects in rule base. Exact defect definitions and optimization operation details are given. The sample shows that the method is feasible in practice.

Copyright

© 2007, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hong-qin Wei
PY  - 2007/10
DA  - 2007/10
TI  - A two-stage rule base optimization method based on combination of classical algorithm and GA for intelligent decision support system
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 734
EP  - 739
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.126
DO  - 10.2991/iske.2007.126
ID  - Wei2007/10
ER  -