Title :
An adaptable system to construct fuzzy decision trees
Author :
Marsala, Christophe ; Bouchon-Meunier, Bernadette
Author_Institution :
Univ. Pierre et Marie Curie, Paris, France
Abstract :
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
Keywords :
adaptive systems; data handling; data mining; decision trees; fuzzy set theory; learning (artificial intelligence); Salammbo system; adaptable system; data handling; data management; data mining; fuzzy decision tree construction; fuzzy set theory; inductive learning methods; machine learning techniques; numerical attributes; operators; symbolic attributes; Artificial intelligence; Data mining; Databases; Decision trees; Delta modulation; Fuzzy systems; Learning systems; Machine learning; Set theory;
Conference_Titel :
Fuzzy Information Processing Society, 1999. NAFIPS. 18th International Conference of the North American
Conference_Location :
New York, NY
Print_ISBN :
0-7803-5211-4
DOI :
10.1109/NAFIPS.1999.781687
