DocumentCode
303345
Title
A fuzzy classifier based on partitioned hyperboxes
Author
Thawonmas, Ruck ; Abe, Shigeo
Author_Institution
Res. Lab., Hitachi Ltd., Ibaraki, Japan
Volume
2
fYear
1996
fDate
3-6 Jun 1996
Firstpage
1097
Abstract
We discuss a method for improving the performance of a fuzzy classifier that approximates class regions in the input space directly using hyperboxes. The proposed method performs partition of the hyperboxes with the aim of preventing under-fitting of the training data due to large hyperboxes. It terminates according to a proposed terminating criterion that prevents over-fitting of the training data due to excessive partition. Experimental results on widely used iris data substantiate the effectiveness of the proposed method
Keywords
fuzzy logic; fuzzy set theory; inference mechanisms; learning (artificial intelligence); pattern classification; class regions; fuzzy classifier; iris data; over-fitting; partitioned hyperboxes; terminating criterion; under-fitting; Data mining; Fuzzy logic; Fuzzy neural networks; Iris; Laboratories; Multi-layer neural network; Neural networks; Neurons; Pattern recognition; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1996., IEEE International Conference on
Conference_Location
Washington, DC
Print_ISBN
0-7803-3210-5
Type
conf
DOI
10.1109/ICNN.1996.549051
Filename
549051
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