An Algorithm To Building A Fuzzy Decision Tree For Data Classification Problem Based On The Fuzziness Intervals Matching

Le Van Tuong Lan, Nguyen Mau Han, Nguyen Cong Hao


Nowaday, on demand to reflect the real world, so we have many imprecise stored business data warehouses. The precise data classification can not solve all the requirements. Thus, fuzy decision tree classification problem have role is important of fuzzy data mining problem.

The fuzzy decision classification based on fuzzy set theory have some limitations derived from the inner selves of it. The hedge algebra with many advantages has become a really useful tool for solving the fuzzy decision tree classification.

However, sample data homogeniseprocess based on quantitative methods ofthe hedge algebra withsome restrictions remain appear because of error in the process and not the result tree truly versatile. So,the fuzzy decision tree obtained not always have high predictable. In this paper, weusing  fuzzinessintervals matching an approachhedge algebra, we proposedthe inductive learning method HAC4.5 fuzzy decision tree to obtain the fuzzy decision tree with high predictable.


Hedge algebra, liguistic, homogenise, fuzzy decision tree, HAC4.5.

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Journal of Computer Science and Cybernetics ISSN: 1813-9663

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