Title :
A fuzzy classifier based on partitioned hyperboxes
Author :
Thawonmas, Ruck ; Abe, Shigeo
Author_Institution :
Res. Lab., Hitachi Ltd., Ibaraki, Japan
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;
Conference_Titel :
Neural Networks, 1996., IEEE International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-3210-5
DOI :
10.1109/ICNN.1996.549051
