Optimal linear combinations of neural networks: an overview

Hashem, Sherif; Schmeiser, Bruce W.; Yih, Yuehwern

doi:10.1109/icnn.1994.374511

Cited by 52 publications

(20 citation statements)

References 8 publications

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“…Many researchers in other fields have applied classifier ensemble methods, which can generate predictions that are theoretically [46] and empirically [7,39] better than those of individual classifiers. However, all existing prediction models of software project risk are based on individual classifiers [1,48,61], and sufficient ensemble models have not been developed.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Cost-sensitive and ensemble-based prediction model for outsourced software project risk prediction

Feng

et al. 2015

Decision Support Systems

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Section: Accepted Manuscriptmentioning

confidence: 99%

Cost-sensitive and ensemble-based prediction model for outsourced software project risk prediction

Feng

et al. 2015

Decision Support Systems

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“…A variety of schemes have been proposed for combining multiple classifiers, such as the majority vote [15] , the weighted average [16] , the Bayes approach [17] , the Dempster-Shafer theory [18] and combination by neural networks [19] . A major issue in the proposed system is how to choose appropriate ensemble of classifiers to be integrated in the multiple classifier system.…”

Section: Algorithm Of Combinationmentioning

confidence: 99%

Research on remote sensing image of land cover classification based on multiple classifier combination

Dai

Liu

2011

Wuhan Univ. J. Nat. Sci.

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This paper proposed an algorithm in which the maximum probability and the weighted average strategy were used for the combination of member classifiers. Using parallel computing， we test the algorithm on a China-Brazil Earth Resources Satellite (CBERS) image for land cover classification. The results show that using three computers in parallel can reduce the classification time by 30%, as compared with using only one computer with a dual core processor. The accuracy of the final image is 93.34%, and Kappa is 0.92. Multiple classifier combination can enhance the precision of the image classification, and parallel computing can increase the speed of calculation so that it becomes possible to process remote sensing images with high efficiency and accuracy.

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“…However, the network may not be the optimum when it is applied to the whole population. Hashem et al [7] proposed using optimal linear combinations of a number of trained neural networks instead of using a single best network. Each component network can have a different architecture and/or training parameters.…”

Section: Multiple Neural Network Approachesmentioning

confidence: 99%

Multiple neural networks for a long term time series forecast

Nguyen

Chan

2004

Neural Computing & Applications

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The artificial neural network (ANN) methodology has been used in various time series prediction applications. However, the accuracy of a neural network model may be seriously compromised when it is used recursively for making long-term multi-step predictions. This study presents a method using multiple ANNs to make a long term time series prediction. A multiple neural network (MNN) model is a group of neural networks that work together to solve a problem. In the proposed MNN approach, each component neural network makes forecasts at a different length of time ahead. The MNN method was applied to the problem of forecasting an hourly customer demand for gas at a compression station in Saskatchewan, Canada. The results showed that a MNN model performed better than a single ANN model for long term prediction.

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Optimal linear combinations of neural networks: an overview

Cited by 52 publications

References 8 publications

Cost-sensitive and ensemble-based prediction model for outsourced software project risk prediction

Cost-sensitive and ensemble-based prediction model for outsourced software project risk prediction

Research on remote sensing image of land cover classification based on multiple classifier combination

Multiple neural networks for a long term time series forecast

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