A Fuzzy Approach to Decision Trees

• Main Authors: Shih-Wei Sun, 孫世偉
• Other Authors: Shian-Shyong Tseng
• Format: Others
• Language: en_US
• Online Access: http://ndltd.ncl.edu.tw/handle/44377695469006604167

碩士 === 國立交通大學 === 資訊科學學系 === 83 === Symbolic learning strategies can usually be divided into two classes. The first class is batch learning strategies, such as ID3 and PRISM. The second class is incremental learning strategies, such as version space. In recent years, the applications of fuzzy logic becomes increasing important in artificial intelligence research fields since it can deal with the problem of imprecise data and is closer to the thinking model of human than the traditional approach. The focus of this thesis is on the merger of fuzzy logic and decision trees. Decision trees and their various algorithms are popular choices in applications to learning and reasoning from feature-based examples. This popularity is due to easily understood processing mechanisms, comprehensibility of the generated knowledge structure, which might be converted to a set of rules and subsequently used in a diagnostic expert system, and wide availability of data in a form of feature descriptions. However, there are still some problems related to the traditional decision. They include the inability to cope with missing data, imprecise information, and measurement errors. The most important sources of the above problem come from continuous attributes and the sensitivity of noise (from imprecise information or measurement errors). To solve the above problem, merging traditional decision trees with the fuzzy processing is proposed. The fuzzy approach to decision trees fuzzifies the numeric data at first, and uses the fuzzy entropy function to select the best feature which is not used. Furthermore, an experiment of fuzzy and non-fuzzy decision tree on Iris Plants Database is done. The result shows that the fuzzy approach is a little better than non-fuzzy approach on accuracy and tree size since the Iris data contains only 150 cases. The future work is to do another experiment on a proper domain and develop a methodology to convert the fuzzy decision tree to a fuzzy rule set.