An adaptable system to construct fuzzy decision trees

Author

Marsala, Christophe ; Bouchon-Meunier, Bernadette

Author_Institution

Univ. Pierre et Marie Curie, Paris, France

fYear

1999

fDate

36342

Firstpage

223

Lastpage

227

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/NAFIPS.1999.781687

Filename

781687