| Summary: | 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.
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