Title

An Adaptive Multiobjective Approach to Evolving ART Architectures

Authors

Authors

A. Kaylani; M. Georgiopoulos; M. Mollaghasemi; G. C. Anagnostopoulos; C. Sentelle;M. Y. Zhong

Comments

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Abbreviated Journal Title

IEEE Trans. Neural Netw.

Keywords

ARTMAP; category proliferation; classification; genetic algorithms; (GAs); genetic operators; machine learning; GENETIC ALGORITHMS; NEURAL-NETWORK; CLASSIFICATION PROBLEMS; PATTERN-CLASSIFICATION; FUZZY ARTMAP; OPTIMIZATION; MACHINE; RULES; SYSTEMS; Computer Science, Artificial Intelligence; Computer Science, Hardware &; Architecture; Computer Science, Theory & Methods; Engineering, ; Electrical & Electronic

Abstract

In this paper, we present the evolution of adaptive resonance theory (ART) neural network architectures (classifiers) using a multiobjective optimization approach. In particular, we propose the use of a multiobjective evolutionary approach to simultaneously evolve the weights and the topology of three well-known ART architectures; fuzzy ARTMAP (FAM), ellipsoidal ARTMAP (EAM), and Gaussian ARTMAP (GAM). We refer to the resulting architectures as MO-GFAM, MO-GEAM, and MO-GGAM, and collectively as MO-GART. The major advantage of MO-GART is that it produces a number of solutions for the classification problem at hand that have different levels of merit [accuracy on unseen data (generalization) and size (number of categories created)]. MO-GART is shown to be more elegant (does not require user intervention to define the network parameters), more effective (of better accuracy and smaller size), and more efficient (faster to produce the solution networks) than other ART neural network architectures that have appeared in the literature. Furthermore, MO-GART is shown to be competitive with other popular classifiers, such as classification and regression tree (CART) and support vector machines (SVMs).

Journal Title

Ieee Transactions on Neural Networks

Volume

21

Issue/Number

4

Publication Date

1-1-2010

Document Type

Article

Language

English

First Page

529

Last Page

550

WOS Identifier

WOS:000276257000001

ISSN

1045-9227

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