Title

An Adaptive Multiobjective Approach To Evolving Art Architectures

Keywords

ARTMAP; Category proliferation; Classification; Genetic algorithms (GAs); Genetic operators; Machine learning

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). © 2006 IEEE.

Publication Date

4-1-2010

Publication Title

IEEE Transactions on Neural Networks

Volume

21

Issue

4

Number of Pages

529-550

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TNN.2009.2037813

Socpus ID

77950859398 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/77950859398

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