Neuro-fuzzy ID3: A method of inducing fuzzy decision trees with linear programming for maximizing entropy and an algebraic method for incremental learning

ID3 is a popular and efficient method of making a decision tree for classification from symbolic data without much computation. Fuzzy reasoning rules in the form of a decision tree, which can be viewed as a fuzzy partition, are obtained by fuzzy ID3. The aims of this paper are: (1) Not only the learning from examples but also the interview with domain specialists are needed for knowledge acquisition in expert systems. In order to avoid dangerous simplification of the tree by discarding partial knowledge of the experts, a measure of uncertainty with maximizing entropy is applied to fuzzy ID3. (2) Basically the tree based learning is nonincremental or single step. An algebraic method to facilitate incremental learning like the neural nets is adapted and the fuzzy decision tree which consists of B-spline membership function is regarded as three layered neural network. (3) Prototypes of expert system for estimation of wheel wear characteristics and surface roughness in abrasive cut-off are developed.