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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 Kang
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.

Funding

Ministry of Trade, Industry and Energy

History

Publication title

Proceedings from the International Conference on Ubiquitous Information Management and Communication

Pagination

1-5

ISBN

9781450363853

Department/School

School of Information and Communication Technology

Publisher

Association for Computing Machinery

Place of publication

United States

Event title

International Conference on Ubiquitous Information Management and Communication

Event Venue

Langkawi, Malaysia

Date of Event (Start Date)

2018-01-05

Date of Event (End Date)

2018-01-07

Rights statement

Copyright 2018 ACM

Repository Status

  • Restricted

Socio-economic Objectives

Information systems, technologies and services not elsewhere classified

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