An adaptable system to construct fuzzy decision trees
C Marsala, B Bouchon-Meunier - 18th International Conference …, 1999 - ieeexplore.ieee.org
18th International Conference of the North American Fuzzy …, 1999•ieeexplore.ieee.org
Nowadays, data mining is an active domain that is linked to data management and machine
learning techniques. However, even if inductive learning methods work well when handling
symbolic attributes, problems arise when considering numerical or numerical-symbolic
(num/symb) attributes. This problem can be solved by introducing tools from fuzzy set theory
to handle such kinds of data. In this paper, we present an adaptable system to construct and
to use fuzzy decision trees by means of several kinds of operators.
learning techniques. However, even if inductive learning methods work well when handling
symbolic attributes, problems arise when considering numerical or numerical-symbolic
(num/symb) attributes. This problem can be solved by introducing tools from fuzzy set theory
to handle such kinds of data. In this paper, we present an adaptable system to construct and
to use fuzzy decision trees by means of several kinds of operators.
Nowadays, data mining is an active domain that is linked to data management and machine learning techniques. However, even if inductive learning methods work well when handling symbolic attributes, problems arise when considering numerical or numerical-symbolic (num/symb) attributes. This problem can be solved by introducing tools from fuzzy set theory to handle such kinds of data. In this paper, we present an adaptable system to construct and to use fuzzy decision trees by means of several kinds of operators.
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