Continuous ID3 algorithm with fuzzy entropy measures

Author

Cios, Krzysztof J. ; Sztandera, Leszek M.

Author_Institution

Toledo Univ., OH, USA

fYear

1992

fDate

8-12 Mar 1992

Firstpage

469

Lastpage

476

Abstract

Fuzzy entropy measures are used to obtain a quick convergence of a continuous ID3 (CID3) algorithm proposed by K.J. Cios and N. Liu (1991), which allows for self-generation of a hierarchical feedforward neural network architecture by converting decision trees into hidden layers of a neural network. To demonstrate the learning capacity of the fuzzy version of the CID3 algorithm, it was tested on difficult spiral data consisting of 192 points, with 96 points for each spiral. One spiral is generated as a reflection of another, making the problem highly not linearly separable. A remarkable decrease in convergence time is achieved by using a fuzzy entropy measure with generalized Dombi operations

Keywords

entropy; feedforward neural nets; fuzzy logic; learning (artificial intelligence); CID3 algorithm; continuous ID3 algorithm; convergence time; decision trees; fuzzy entropy measures; generalized Dombi operations; hidden layers; hierarchical feedforward neural network architecture; neural network architecture self-generation; spiral data; Convergence; Decision trees; Entropy; Feedforward neural networks; Fuzzy neural networks; Fuzzy sets; Neural networks; Performance evaluation; Q measurement; Spirals; Testing; Time measurement; Velocity measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 1992., IEEE International Conference on

Conference_Location

San Diego, CA

Print_ISBN

0-7803-0236-2

Type

conf

DOI

10.1109/FUZZY.1992.258659

Filename

258659