An ordering algorithm for pattern presentation in fuzzy ARTMAP that tends to improve generalization performance

Dagher, Issam; Georgiopoulos, Michael; Heileman, Gregory L.; Bebis, George

doi:10.1109/72.774217

Cited by 85 publications

(69 citation statements)

References 13 publications

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Order By: Relevance

“…One troublesome issue of using fuzzy ARTMAP classifiers is the network inherent sensitivity to the presentation order of the training data [7], [22]. In order to overcome this problem, we implemented and compared two strategies named averaging and voting.…”

Section: A the Methodologymentioning

confidence: 99%

Accurate and Fast Off and Online Fuzzy ARTMAP-Based Image Classification With Application to Genetic Abnormality Diagnosis

Vigdor

Lerner

2006

IEEE Trans. Neural Netw.

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Abstract-We propose and investigate the fuzzy ARTMAP neural network in off and online classification of fluorescence in situ hybridization image signals enabling clinical diagnosis of numerical genetic abnormalities. We evaluate the classification task (detecting a several abnormalities separately or simultaneously), classifier paradigm (monolithic or hierarchical), ordering strategy for the training patterns (averaging or voting), training mode (for one epoch, with validation or until completion) and model sensitivity to parameters. We find the fuzzy ARTMAP accurate in accomplishing both tasks requiring only very few training epochs. Also, selecting a training ordering by voting is more precise than if averaging over orderings. If trained for only one epoch, the fuzzy ARTMAP provides fast, yet stable and accurate learning as well as insensitivity to model complexity. Early stop of training using a validation set reduces the fuzzy ARTMAP complexity as for other machine learning models but cannot improve accuracy beyond that achieved when training is completed. Compared to other machine learning models, the fuzzy ARTMAP does not loose but gain accuracy when overtrained, although increasing its number of categories. Learned incrementally, the fuzzy ARTMAP reaches its ultimate accuracy very fast obtaining most of its data representation capability and accuracy by using only a few examples. Finally, the fuzzy ARTMAP accuracy for this domain is comparable with those of the multilayer perceptron and support vector machine and superior to those of the naive Bayesian and linear classifiers.Index Terms-Fluorescence in situ hybridization (FISH), fuzzy ARTMAP neural network (NN), genetic abnormality diagnosis, image classification, off-and online learning.

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Section: A the Methodologymentioning

confidence: 99%

Accurate and Fast Off and Online Fuzzy ARTMAP-Based Image Classification With Application to Genetic Abnormality Diagnosis

Vigdor

Lerner

2006

IEEE Trans. Neural Netw.

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“…When the first data sample is put into the blank network model, the first cluster node is built as one fault category by the sample and the node weight is the sample itself. When the next input sample enters the model, the similarity between the second input sample and the first cluster node of the model is calculated and then compared with the vigilance parameter; if the similarity is larger than the vigilance parameter, the input sample is classified into the first cluster, and the corresponding weight vector of the cluster node is updated by (8); otherwise, the second cluster node is produced. When the third input sample enters the model, it is compared with all the produced cluster nodes.…”

Section: Fault Diagnosismentioning

confidence: 99%

“…As a solution to this problem, the adaptive resonance theory (ART) networks were developed and have been applied to the field of pattern recognition and fault diagnosis [4]. Because of the superiority of ART network, some ART models such as ART2 and fuzzy ART have been developed and applied to the field of fault diagnosis [5][6][7][8]. Furthermore, some classification methods with the improvement of ART, such as the combination of 2 Mathematical Problems in Engineering ART and Yu's norm, have been applied well to the field of fault diagnosis, which can deal with the uncertainty problem through the fuzzy formalism [9].…”

Section: Introductionmentioning

confidence: 99%

Based on Soft Competition ART Neural Network Ensemble and Its Application to the Fault Diagnosis of Bearing

Yang

Zhang

et al. 2017

Mathematical Problems in Engineering

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This paper presents a novel method for fault diagnosis based on an improved adaptive resonance theory (ART) neural network and ensemble technique. The method consists of three stages. Firstly, the improved ART neural network is comprised of the soft competition technique based on fuzzy competitive learning (FCL) and ART based on Yu's norm, the neural nodes in the competition layer are trained according to the degree of membership between the mode node and the input, and then fault samples are classified in turn. Secondly, with the distance evaluation technique, the optimal features are obtained from the statistical characteristics of original signals and wavelet coefficients. Finally, the optimal features are input into the neural network ensemble (NNE) based on voting method to identify the different fault categories. The proposed method is applied to the fault diagnosis of rolling element bearings, and testing results show that the neural network ensemble can reliably classify different fault categories and the degree of faults, which has a better classification performance compared with the single neural network.

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“…Mostly conventional neural networks suffers from plasticity-stability dilemma, i.e. the information related to the plasticity or adaptivity to the new inputs or change in inputs at the same time stable in response [75] [76]. The fuzzy ARTMAP structure shown in Figure 8 addresses this dilemma by incorporating a feedback mechanism between the competitive and input layers to allow new information to be learned without eliminating previously obtained knowledge, in this it becomes more stable and shows a faster convergence capability [77].…”

Section: Fuzzy Artmap Based Modelmentioning

confidence: 99%

A Review of Price Forecasting Problem and Techniques in Deregulated Electricity Markets

Singh¹,

Mohanty²

2015

JPEE

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In deregulated electricity markets, price forecasting is gaining importance between various market players in the power in order to adjust their bids in the day-ahead electricity markets and maximize their profits. Electricity price is volatile but non random in nature making it possible to identify the patterns based on the historical data and forecast. An accurate price forecasting method is an important factor for the market players as it enables them to decide their bidding strategy to maximize profits. Various models have been developed over a period of time which can be broadly classified into two types of models that are mainly used for Electricity Price forecasting are: 1) Time series models; and 2) Simulation based models; time series models are widely used among the two, for day ahead forecasting. The presented work summarizes the influencing factors that affect the price behavior and various established forecasting models based on time series analysis, such as Linear regression based models, nonlinear heuristics based models and other simulation based models.

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An ordering algorithm for pattern presentation in fuzzy ARTMAP that tends to improve generalization performance

Cited by 85 publications

References 13 publications

Accurate and Fast Off and Online Fuzzy ARTMAP-Based Image Classification With Application to Genetic Abnormality Diagnosis

Accurate and Fast Off and Online Fuzzy ARTMAP-Based Image Classification With Application to Genetic Abnormality Diagnosis

Based on Soft Competition ART Neural Network Ensemble and Its Application to the Fault Diagnosis of Bearing

A Review of Price Forecasting Problem and Techniques in Deregulated Electricity Markets

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