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

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 <i>If-Then</i> 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 <i>Data-Driven Knowledge Acquisition Tool</i> (DDKAT) is developed.