KEM-DT: A Knowledge Engineering Methodology to Produce an Integrated Rules Set using Decision Tree Classifiers
Article No.: 35, Pages 1 - 5
Abstract
In artificial intelligence, knowledge engineering is one of the key research areas in which knowledge-based systems are developed to solve the real-world problems and helps in decision making. For constructing a rule-based knowledge base, normally single decision tree classifier is used to produce If-Then rules (i.e. production rules). In the health-care domain, these machine generated rules are normally not well accepted by domain experts due to knowledge credibility issues. Keeping in view these facts, this paper proposes a knowledge engineering methodology called KEM-DT, which generates classification models of multiple decision trees, transforms them into production rules sets, and lastly, after rules verification and validation from an expert, integrates them to construct an integrated as well as a credible rule-based knowledge base. Finally, in order to realize the KEM-DT methodology, a Data-Driven Knowledge Acquisition Tool (DDKAT) is developed.
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Index Terms
- KEM-DT: A Knowledge Engineering Methodology to Produce an Integrated Rules Set using Decision Tree Classifiers
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Published In
January 2018
628 pages
ISBN:9781450363853
DOI:10.1145/3164541
Copyright © 2018 ACM.
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- SKKU: SUNGKYUNKWAN UNIVERSITY
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Association for Computing Machinery
New York, NY, United States
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Published: 05 January 2018
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- Research-article
- Research
- Refereed limited
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- Ministry of Trade, Industry and Energy (MOTIE, Korea).
- National Research Foundation of Korea (NRF)
- MSIT (Ministry of Science and ICT)
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IMCOM '18
IMCOM '18: The 12th International Conference on Ubiquitous Information Management and Communication
January 5 - 7, 2018
Langkawi, Malaysia
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IMCOM '18 Paper Acceptance Rate 100 of 255 submissions, 39%;
Overall Acceptance Rate 213 of 621 submissions, 34%
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