Safe-μARTMAP: a new solution for reducing category proliferation in fuzzy ARTMAP

Author

Gómez-Sánchez, E. ; Dimitriadis, Y.A. ; Cano-Izquierdo, J.M. ; López-Coronado, J.

Author_Institution

Dept. of T.S.C.I.T., Valladolid Univ., Spain

Volume

2

fYear

2001

fDate

2001

Firstpage

1197

Abstract

μARTMAP is a neural network architecture that addresses the category proliferation problem present in fuzzy ARTMAP, by encouraging the creation of large hyperboxes. However, under certain characteristics of the classification task, this principle can be inadequate, namely if some classes have their patterns distributed in several isolated regions, far apart in the input space. Here we propose Safe-μARTMAP, a generalization of μARTMAP that limits the growth of a category in response to a single pattern, so that large hyperboxes are not created under these conditions. Experimental results confirm that the performance improves in some synthetic and real world tasks

Keywords

ART neural nets; category theory; fuzzy neural nets; generalisation (artificial intelligence); neural net architecture; pattern classification; Safe-μARTMAP; category proliferation; fuzzy ARTMAP; generalization; neural network architecture; pattern classification; Fuzzy neural networks; Intelligent networks; Modular construction; Neural networks; Pattern matching; Stability; Subspace constraints; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-7044-9

Type

conf

DOI

10.1109/IJCNN.2001.939531

Filename

939531