Interactive improvement of decision trees through flaw analysis and interpretation

✍ Scribed by Katsuhiko Tsujino; Vlad G. Dabija; Shogo Nishida

• Publisher: Elsevier Science
• Year: 1996
• Tongue: English
• Weight: 417 KB
• Volume: 45
• Category: Article
• ISSN: 1071-5819
• DOI: 10.1006/ijhc.1996.0065

✦ Synopsis

This paper describes a framework for knowledge acquisition based on analysing and interpreting flaws in decision trees . The decision trees inductively learned are analysed using domain and task specific knowledge to detect improper states called flaws . These are further used to formulate questions to eliminate the flaws by stimulating the acquisition of new examples and domain knowledge for a new induction cycle . To facilitate this process we frame a unified theory in the classification trees' paradigm arguing : (1) what means to have a good / bad tree ;

(2) why it is good / bad ; and (3) how to obtain a better one . We also describe some experimental results of applying this framework to a domain knowledge acquisition system named KAISER and its meta-learner for the decision trees domain theory which build this theory by keeping track of the experts' response of domain level interaction . 499