Parallel consensual neural networks

Benediktsson, J.A.; Sveinsson, Jóhannes R.; Ersoy, Okan K.; Swain, P. H.

doi:10.1109/72.554191

Cited by 151 publications

(61 citation statements)

References 26 publications

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“…There are four common strategies to determine the weights of base models w i : simple averaging, simple, mean squares error (MSE), stacked regression, and error-variance-based weighting [44]. Besides the individual feature of different strategies, the existing integrated technique is built on linear assumption.…”

Section: Non-linear Neural-network Metamodelmentioning

confidence: 99%

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Xiao¹,

Xiao²,

Lu³

et al. 2014

IJCIS

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Stock e-exchange prices forecasting is an important financial problem that is receiving increasing attention. This study proposes a novel three-stage nonlinear ensemble model. In the proposed model, three different types of neural-network based models, i.e. Elman network, generalized regression neural network (GRNN) and wavelet neural network (WNN) are constructed by three non-overlapping training sets and are further optimized by improved particle swarm optimization (IPSO). Finally, a neural-network-based nonlinear meta-model is generated by learning three neural-network based models through support vector machines (SVM) neural network. The superiority of the proposed approach lies in its flexibility to account for potentially complex nonlinear relationships. Three daily stock indices time series are used for validating the forecasting model. Empirical results suggest the ensemble ANNs-PSO-GA approach can significantly improve the prediction performance over other individual models and linear combination models listed in this study.

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Section: Non-linear Neural-network Metamodelmentioning

confidence: 99%

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Xiao¹,

Xiao²,

Lu³

et al. 2014

IJCIS

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“…If , the final decision system will degenerate to the simple "majority voting" approach. One drawback of the linear opinion pool is that it is not Bayesian [23]. It was found [23], for some cases, that one neural network may dominate the final decision.…”

Section: E Final Decision Making or Fusion Subsystemmentioning

confidence: 99%

“…The basic idea behind this theory is to combine the outputs of different neural networks with different weights that can reflect the relative importance of the classification result of that particular network. Several different implementation schemes have been studied and perhaps the most widely used one is the linear opinion pool [23].…”

Section: E Final Decision Making or Fusion Subsystemmentioning

confidence: 99%

Detection of mines and minelike targets using principal component and neural-network methods

Miao

Azimi-Sadjadi

Tan³

et al. 1998

IEEE Trans. Neural Netw.

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Abstract-This paper introduces a new system for real-time detection and classification of arbitrarily scattered surface-laid mines from multispectral imagery data of a minefield. The system consists of six channels which use various neural-network structures for feature extraction, detection, and classification of targets in six different optical bands ranging from near UV to near IR. A single-layer autoassociative network trained using the recursive least square (RLS) learning rule was employed in each channel to perform feature extraction. Based upon the extracted features, two different neural-network architectures were used and their performance was compared against the standard maximum likelihood (ML) classification scheme. The outputs of the detector/classifier network in all the channels were fused together in a final decision-making system. Two different final decision making schemes using the majority voting and weighted combination based on consensual theory were considered. Simulations were performed on real data for six bands and on several images in order to account for the variations in size, shape, and contrast of the targets and also the signalto-clutter ratio. The overall results showed the promise of the proposed system for detection and classification of mines and minelike tagets.

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“…A Bayesian approach has also been used in Consensus based classification of multisource remote sensing data [10,9,19], outperforming conventional multivariate methods for classification. To overcome the problem of the independence assumption (that is unrealistic in most cases), the Behavior-Knowledge Space (BKS) method [56] considers each possible combination of class labels, filling a look-up table using the available data set, but this technique requires a huge volume of training data.…”

Section: Non-generative Ensemblesmentioning

confidence: 99%

Ensembles of Learning Machines

2002

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Abstract. Ensembles of learning machines constitute one of the main current directions in machine learning research, and have been applied to a wide range of real problems. Despite of the absence of an unified theory on ensembles, there are many theoretical reasons for combining multiple learners, and an empirical evidence of the effectiveness of this approach. In this paper we present a brief overview of ensemble methods, explaining the main reasons why they are able to outperform any single classifier within the ensemble, and proposing a taxonomy based on the main ways base classifiers can be generated or combined together.

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Parallel consensual neural networks

Cited by 151 publications

References 26 publications

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

Detection of mines and minelike targets using principal component and neural-network methods

Ensembles of Learning Machines

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