GFAM: Evolving Fuzzy ARTMAP neural networks

Al-Daraiseh, Ahmad; Kaylani, Assem; Georgiopoulos, Michael; Mollaghasemi, Mansooreh; Wu, Annie S.; Anagnostopoulos, Georgios C.

doi:10.1016/j.neunet.2007.05.006

Cited by 13 publications

(9 citation statements)

References 35 publications

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“…It is also worth mentioning that the categories in FAM, EAM and GAM are allowed to expand up to a point allowed by a threshold, controlled by a network parameter denoted as the baseline vigilance parameter (ρ a ). This parameter assumes values in the interval [0,1]. Small values of this parameter allow the creation of large categories, while large values of this parameter allow the creation of small categories.…”

Section: The Artmap Architecturesmentioning

confidence: 99%

“…ENETIC ARTMAP (GART) architectures were introduced in [1] and [2]. It was shown that the these architectures were able to achieve very competitive classification accuracy and exceptionally small-size classifiers when compared to other classifiers in the literature (see [3], [4]).…”

mentioning

confidence: 99%

“…In [11] review was conducted to summarize the prior efforts that aimed at combining evolutionary optimization algorithms with neural networks. The evolution of ART architectures with genetic algorithms was introduced for the first time in [1] and extended in [2]. Genetic ART starts with a population of trained ART networks, whose number of nodes in the hidden layer and the values of the interconnection weights converging to these nodes are fully determined (at the beginning of the evolution) by ART's training rules.…”

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confidence: 99%

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Efficient evolution of ART neural networks

Kaylani

Georgiopoulos

Mollaghasemi

et al. 2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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Abstract-Genetic algorithms have been used to evolve several neural network architectures. In a previous effort, we introduced the evolution of three well known ART architects; Fuzzy ARTMAP (FAM), Ellipsoidal ARTMAP (EAM) and Gaussian ARTMAP (GAM). The resulting architectures were shown to achieve competitive generalization and exceptionally small size. A major concern regarding these architectures, and any evolved neural network architecture in general, is the added overhead in terms of computational time needed to produce the finally evolved network. In this paper we investigate ways of reducing this computational overhead by reducing the computations needed for the calculation of the fitness value of the evolved ART architectures. The results obtained in this paper can be directly extended to many other evolutionary neural network architectures, beyond the studied evolution of ART neural network architectures.

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Section: The Artmap Architecturesmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Efficient evolution of ART neural networks

Kaylani

Georgiopoulos

Mollaghasemi

et al. 2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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“…To alleviate these problems, we introduced genetic fuzzy ARTMAP (GFAM) in [5] and [6]. GFAM uses a genetic algorithm (GA) (see [7]) to evolve simultaneously the weights, as well as the topology of ART neural networks.…”

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confidence: 99%

“…GFAM uses a genetic algorithm (GA) (see [7]) to evolve simultaneously the weights, as well as the topology of ART neural networks. In [6], we extended the ideas of genetically engineering FAM (GFAM, in [5]) to EAM and GAM, and introduced several improvements, which resulted in significant gains in terms of efficiency.…”

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confidence: 99%

An Adaptive Multiobjective Approach to Evolving ART Architectures

Kaylani

Georgiopoulos

Mollaghasemi

et al. 2010

IEEE Trans. Neural Netw.

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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).

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Advanced Developments and Applications of the Fuzzy ARTMAP Neural Network in Pattern Classification

Lerner

Guterman

Studies in Computational Intelligence

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Abstract. Since its inception in 1992, the fuzzy ARTMAP (FAM) neural network (NN) has attracted researchers' attention as a fast, accurate, off and online pattern classifier. Since then, many studies have explored different issues concerning FAM optimization, training and evaluation, e.g., model sensitivity to parameters, ordering strategy for the presentation of the training patterns, training method and method of predicting the classification accuracy. Other studies have suggested variants to FAM to improve its generalization capability or overcome the prime limitation of the model, which is category proliferation (i.e., model complexity that increases with data complexity). Category proliferation is pronounced in problems that are noisy or contain a large degree of class statistical overlapping. In many investigations, FAM was improved by incorporating elements of optimization theory, Bayes' decision theory, evolutionary learning, and cluster analysis. Due to its appealing characteristics, FAM and its variants have been applied extensively and successfully to real-world classification problems. Numerous applications were reported in, for example, the processing of signals from different sources, images, speech, and text; recognition of speakers, image objects, handwritten, and genetic abnormalities; and medical and fault diagnoses. When compared to other state-of-the-art machine learning classifiers, FAM and its variants showed superior speed and ease of training, and in most cases they delivered comparable classification accuracy.

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GFAM: Evolving Fuzzy ARTMAP neural networks

Cited by 13 publications

References 35 publications

Efficient evolution of ART neural networks

Efficient evolution of ART neural networks

An Adaptive Multiobjective Approach to Evolving ART Architectures

Advanced Developments and Applications of the Fuzzy ARTMAP Neural Network in Pattern Classification

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