Fuzzy decision trees and numerical attributes

Author

Zeidler, Jens ; Schlosser, Michael ; Ittner, Andreas ; Posthoff, Christian

Author_Institution

Dept. of Comput. Sci., Chemnitz Univ. of Technol., Germany

Volume

2

fYear

1996

fDate

8-11 Sep 1996

Firstpage

985

Abstract

Classical crisp decision trees (DT) are widely applied to classification tasks. Nevertheless, there are also a lot of fuzzy decision tree (FDT) solutions. Sometimes fuzzy borders for discretisation of continuous-valued attributes are used. The induction of FDT in these solutions need some parameters from the expert or user. An automatically generated membership function for discretisation of continuous-valued attributes is described in this paper. An example with the decision tree construction and the unseen data classification is given. The results of crisp and fuzzy decision trees are compared at the end

Keywords

decision theory; fuzzy logic; learning (artificial intelligence); automatically generated membership function; classical crisp decision trees; classification tasks; continuous-valued attributes; data classification; fuzzy decision trees; numerical attributes; Artificial intelligence; Chemical technology; Classification tree analysis; Computer science; Decision trees; Electronic mail; Entropy; Induction generators; Mathematics; Partitioning algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on

Conference_Location

New Orleans, LA

Print_ISBN

0-7803-3645-3

Type

conf

DOI

10.1109/FUZZY.1996.552312

Filename

552312