NEURO-FUZZY ID3 - A METHOD OF INDUCING FUZZY DECISION TREES WITH LINEAR-PROGRAMMING FOR MAXIMIZING ENTROPY AND AN ALGEBRAIC-METHOD FOR INCREMENTAL LEARNING
H. Ichihashi et al., NEURO-FUZZY ID3 - A METHOD OF INDUCING FUZZY DECISION TREES WITH LINEAR-PROGRAMMING FOR MAXIMIZING ENTROPY AND AN ALGEBRAIC-METHOD FOR INCREMENTAL LEARNING, Fuzzy sets and systems, 81(1), 1996, pp. 157-167
Citations number
22
Categorie Soggetti
Computer Sciences, Special Topics","System Science",Mathematics,"Statistic & Probability",Mathematics,"Computer Science Theory & Methods
ID3 is a popular and efficient method of making a decision tree for cl
assification from symbolic data without much computation. Fuzzy reason
ing rules in the form of a decision tree, which can be viewed as a fuz
zy partition, are obtained by fuzzy ID3. The aims of this paper are: (
1) Not only the learning from examples but also the interview with dom
ain 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 maxim
izing entropy is applied to fuzzy ID3. (2) Basically the tree based le
arning is nonincremental or single step. An algebraic method to facili
tate incremental learning like the neural nets is adapted and the fuzz
y decision tree which consists of B-spline membership function is rega
rded 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.