Rule induction for complete information systems in knowledge acquisition and classification

Hong Zhen Zheng; Dian Hui Chu; De Chen Zhan

doi:10.1007/11739685_29

Rule induction for complete information systems in knowledge acquisition and classification

Hong Zhen Zheng^*
, Dian Hui Chu
, De Chen Zhan

^*Corresponding author for this work

Faculty of Computing

Harbin Institute of Technology Weihai

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper proposes a modified rule generation (MRG) algorithm and rule induction prototype(RGRIP). It can help the decision-maker predict the outcomes of new cases effectively. Not only MRG algorithm provides a very fast and effective way to generate a minimal set of rule reducts from which "certain" rules can be induced, but also produces as a byproduct a revised decision label T from which "possible" rules could be conveniently induced. Then, combining the MRG algorithm with the rule induction schemes, we proposed a rule generation and rule induction prototype(RGRIP) that can automatically generate a minimal set of reducts and induce all certain rules as well as possible rule with all their plausibility indices. In term of ability to deal with uncertainty and inconsistency in the data set, RGRIP approach appears simplicity and conciseness in the process of its usage. The approach is efficient and effective in dealing with large data sets.

Original language	English
Title of host publication	Advances in Machine Learning and Cybernetics - 4th International Conference, ICMLC 2005, Revised Selected Papers
Publisher	Springer Verlag
Pages	278-284
Number of pages	7
ISBN (Print)	3540335846, 9783540335849
DOIs	https://doi.org/10.1007/11739685_29
State	Published - 2006
Event	4th International Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China Duration: 18 Aug 2005 → 21 Aug 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3930 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th International Conference on Machine Learning and Cybernetics, ICMLC 2005
Country/Territory	China
City	Guangzhou
Period	18/08/05 → 21/08/05

Access to Document

10.1007/11739685_29

Cite this

Zheng, H. Z., Chu, D. H., & Zhan, D. C. (2006). Rule induction for complete information systems in knowledge acquisition and classification. In Advances in Machine Learning and Cybernetics - 4th International Conference, ICMLC 2005, Revised Selected Papers (pp. 278-284). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3930 LNAI). Springer Verlag. https://doi.org/10.1007/11739685_29

Zheng, Hong Zhen ; Chu, Dian Hui ; Zhan, De Chen. / Rule induction for complete information systems in knowledge acquisition and classification. Advances in Machine Learning and Cybernetics - 4th International Conference, ICMLC 2005, Revised Selected Papers. Springer Verlag, 2006. pp. 278-284 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Zheng, HZ, Chu, DH & Zhan, DC 2006, Rule induction for complete information systems in knowledge acquisition and classification. in Advances in Machine Learning and Cybernetics - 4th International Conference, ICMLC 2005, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3930 LNAI, Springer Verlag, pp. 278-284, 4th International Conference on Machine Learning and Cybernetics, ICMLC 2005, Guangzhou, China, 18/08/05. https://doi.org/10.1007/11739685_29

Rule induction for complete information systems in knowledge acquisition and classification. / Zheng, Hong Zhen; Chu, Dian Hui ; Zhan, De Chen.
Advances in Machine Learning and Cybernetics - 4th International Conference, ICMLC 2005, Revised Selected Papers. Springer Verlag, 2006. p. 278-284 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3930 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This paper proposes a modified rule generation (MRG) algorithm and rule induction prototype(RGRIP). It can help the decision-maker predict the outcomes of new cases effectively. Not only MRG algorithm provides a very fast and effective way to generate a minimal set of rule reducts from which "certain" rules can be induced, but also produces as a byproduct a revised decision label T from which "possible" rules could be conveniently induced. Then, combining the MRG algorithm with the rule induction schemes, we proposed a rule generation and rule induction prototype(RGRIP) that can automatically generate a minimal set of reducts and induce all certain rules as well as possible rule with all their plausibility indices. In term of ability to deal with uncertainty and inconsistency in the data set, RGRIP approach appears simplicity and conciseness in the process of its usage. The approach is efficient and effective in dealing with large data sets.

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Zheng HZ, Chu DH , Zhan DC. Rule induction for complete information systems in knowledge acquisition and classification. In Advances in Machine Learning and Cybernetics - 4th International Conference, ICMLC 2005, Revised Selected Papers. Springer Verlag. 2006. p. 278-284. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11739685_29

Rule induction for complete information systems in knowledge acquisition and classification

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