Fuzzy ARTMAP dynamic decay adjustment: An improved fuzzy ARTMAP model with a conflict resolving facility

Tan, Shing Chiang; Rao, M. V. C.; Lim, Chee Peng

doi:10.1016/j.asoc.2007.03.006

Cited by 17 publications

(15 citation statements)

References 57 publications

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“…(20) can be obtained by putting the value of x into kernel k . The value of k (x) can be evaluated by setting the derivative of k (s) in Eq.…”

Section: Model Predictionmentioning

confidence: 99%

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An incremental adaptive neural network model for online noisy data regression and its application to compartment fire studies

Lee

2011

Applied Soft Computing

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“…(20) can be obtained by putting the value of x into kernel k . The value of k (x) can be evaluated by setting the derivative of k (s) in Eq.…”

Section: Model Predictionmentioning

confidence: 99%

“…In general, like the variants [20][21][22] of Adaptive Resonance Theory (ART) [23][24][25], this clustering approach is developed on the basis of the sequential leader clustering algorithm [26]. Such variants of ART have also been widely adopted in different engineering applications [27][28][29].…”

Section: Clusteringmentioning

confidence: 99%

An incremental adaptive neural network model for online noisy data regression and its application to compartment fire studies

Lee

2011

Applied Soft Computing

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“…In this case, the original FAM network does not have an ability to handle conflicts resulting from overlapping prototypes of different classes in the input space. In our previous work (Tan et al 2008), a hybrid model combining FAM and the Dynamic Decay Adjustment (DDA) algorithm for coping with overlapping prototypes of different classes during training has been proposed. The conflict-resolving network, known as FAMDDA, has demonstrated good performances in several benchmark studies.…”

Section: Introductionmentioning

confidence: 99%

Integration of supervised ART-based neural networks with a hybrid genetic algorithm

Tan

Lim

2010

Soft Comput

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In this paper, two evolutionary artificial neural network (EANN) models that are based on integration of two supervised adaptive resonance theory (ART)-based artificial neural networks with a hybrid genetic algorithm (HGA) are proposed. The search process of the proposed EANN models is guided by a knowledge base established by ART with respect to the training data samples. The EANN models explore the search space for ''coarse'' solutions, and such solutions are then refined using the local search process of the HGA. The performances of the proposed EANN models are evaluated and compared with those from other classifiers using more than ten benchmark data sets. The applicability of the EANN models to a real medical classification task is also demonstrated. The results from the experimental studies demonstrate the effectiveness and usefulness of the proposed EANN models in undertaking pattern classification problems.

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“…Also, a stopping criterion is determined that is based on a maximum number of iterations and a maximum number of iterations without fitness improvement. Another algorithm designed to eliminate category proliferation is that of Tan et al [46]. The authors suggest what they call a "conflict-resolving network" based on FAM and a mechanism of dynamic decay adjustment, i.e., FAMDDA.…”

Section: Fam-based New Algorithmsmentioning

confidence: 99%

“…FAMDDA [46] was tested against FAM on UCI Iris, Wisconsin breast cancer, and Statlog image segmentation databases, where it demonstrated an average improvement of around 1% to the FAM classification accuracies for each of these databases. FAMDDA also improved FAM accuracy in monitoring the conditions of a circulating water system in a power generation plant.…”

Section: Experimental Evaluation Of Fam-based Algorithmsmentioning

confidence: 99%

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.

show abstract

Fuzzy ARTMAP dynamic decay adjustment: An improved fuzzy ARTMAP model with a conflict resolving facility

Cited by 17 publications

References 57 publications

An incremental adaptive neural network model for online noisy data regression and its application to compartment fire studies

An incremental adaptive neural network model for online noisy data regression and its application to compartment fire studies

Integration of supervised ART-based neural networks with a hybrid genetic algorithm

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

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