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KEM-DT: a knowledge engineering methodology to produce an integrated rules set decision tree classifiers

Citation

Ali, M and Lee, S and Kang, BH, KEM-DT: a knowledge engineering methodology to produce an integrated rules set decision tree classifiers, Proceedings from the International Conference on Ubiquitous Information Management and Communication, 5-7 January 2018, Langkawi, Malaysia, pp. 1-5. ISBN 9781450363853 (2018) [Refereed Conference Paper]


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Copyright Statement

Copyright 2018 ACM

DOI: doi:10.1145/3164541.3164640

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.

Item Details

Item Type:Refereed Conference Paper
Keywords:knowledge engineering, decision tree, classi cation model, model translation, production rule
Research Division:Information and Computing Sciences
Research Group:Library and Information Studies
Research Field:Information Retrieval and Web Search
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Information Processing Services (incl. Data Entry and Capture)
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:124505
Year Published:2018
Deposited By:Information and Communication Technology
Deposited On:2018-02-23
Last Modified:2019-02-25
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