File(s) under permanent embargo
KEM-DT: a knowledge engineering methodology to produce an integrated rules set decision tree classifiers
conference contribution
posted on 2023-05-23, 13:12 authored by Ali, M, Lee, S, Byeong KangByeong KangIn 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.
Funding
Ministry of Trade, Industry and Energy
History
Publication title
Proceedings from the International Conference on Ubiquitous Information Management and CommunicationPagination
1-5ISBN
9781450363853Department/School
School of Information and Communication TechnologyPublisher
Association for Computing MachineryPlace of publication
United StatesEvent title
International Conference on Ubiquitous Information Management and CommunicationEvent Venue
Langkawi, MalaysiaDate of Event (Start Date)
2018-01-05Date of Event (End Date)
2018-01-07Rights statement
Copyright 2018 ACMRepository Status
- Restricted