A Fuzzy Knowledge Discovery Model Using Fuzzy Decision Tree and Fuzzy Adaptive Learning Control Network

• Main Authors: Tso-Lin Chen, 陳作琳
• Other Authors: Kung-JengWang
• Format: Others
• Language: zh-TW
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/97631994096302009774

碩士 === 中原大學 === 工業工程研究所 === 91 === To explore business information and operation experience from relational databases is a challenge, because many cause-effect relationships and business rules are fuzzy. It is therefore difficult for a decision-maker to discover important factors. At first, we defined fuzzy sets of the membership functions by Dodgson’s function and quartile statistic. Next, we developed a data-mining model base on both the fuzzy decision tree and fuzzy adaptive learning control network—these two concepts help generate concrete rules. This research adopted a decision-tree based learning algorithm and back-propagation neuro network to develop a fuzzy decision tree and fuzzy adaptive learning control network. In order to refine rules, we took the advantage of the Chi-square test of homogeneity to reduce the connection of weight. This research also used the Prediction of Tardiness in Semi-conductor Testing and the Prediction of Grades by an Advanced General Knowledge Course as samples. The results showed that the two models generated a compact fuzzy rule-base that yielded high accuracy.