Inducing Fuzzy Decision Trees in Non-Deterministic Domains using CHAID

Most decision tree induction methods used for extracting knowledge in classification problems are unable to deal with uncertainties embedded within the data, associated with human thinking and perception. This paper describes the development of a novel tree induction algorithm which improves the classification accuracy of decision tree induction in non-deterministic domains. The research involved applies the principles of fuzzy theory to the CHAID (Chi-Square Automatic Interaction Detection) algorithm in order to soften the sharp decision boundaries which are inherent in traditional decision tree algorithms. CHAID is a decision tree induction algorithm with the main feature of significance testing at each level, leading to the production of trees which require no pruning. The application of fuzzy logic to CHAID decision trees can represent classification knowledge more naturally and inline with human thinking and are more robust when it comes to handling imprecise, missing or conflict...

• Type: Conference
• Year: 2004
• Where: FLAIRS
• Authors: Jay Fowdar, Zuhair Bandar, Keeley A. Crockett
• Keywords: Artificial Intelligence, Decision Tree Induction, Decision Trees, FLAIRS 2004, Tree Induction Algorithm