Neural network ensembles: combining multiple models for enhanced performance using a multistage approach

Yang, Shuang; Browne, Antony

doi:10.1111/j.1468-0394.2004.00285.x

Cited by 72 publications

(38 citation statements)

References 27 publications

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“…The use of multiple neural systems was described in the work of Sharkey (1998). A recent review on neural network ensembles can be found in (Yang and Browne 2001).…”

Section: Ensemble Neural Networkmentioning

confidence: 99%

“…Different from neural network ensembles and modular neural network approaches, cooperative modular neural networks can decompose automatically and combines adaptively individual neural network models so that a global optimal solution of the original problem can be obtained. Reported results show that the cooperative modular neural networks can be well applied to classification and pattern recognition (Auda and Kamel 1997a, b, 1998a, b, 1999Zhang 2000;Lu and Ito 1999;Yang and Browne 2001;Oh and Suen 2002;Melin et al 2005;Fogelman-Soulie 1993;Hodge et al 1999;Kamel 1999;Alexandre et al 2001;Ozawa 1998;Islam et al 2003). Specially, in recent decade, as special one class of cooperative modular neural networks, cooperative recurrent modular neural networks for constrained optimization have been developed and well studied (Rodríguez-Vázquez et al 1990;Glazos et al 1998;Zhang and Constantinides 1992;He and Sun 2001;Tao and Fang 2000;Xia and Wang 1995, b, 2001, b, 2005Xia 1996aXia , b, 1997Xia , 2003Xia , 2004Xia et al 2002aXia et al , b, 2004aXia et al , b, 2005Xia et al , 2007Wang et al 2000;Tan et al 2000;Anguita and Boni 2002;Zhang et al 2003;Feng 2004, 2006;Kamel 2007a, b, c, d, 2008;Tao et al 2001;Leung et al 2001).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cooperative recurrent modular neural networks for constrained optimization: a survey of models and applications

Kamel

Xia

2008

Cogn Neurodyn

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Constrained optimization problems arise in a wide variety of scientific and engineering applications. Since several single recurrent neural networks when applied to solve constrained optimization problems for real-time engineering applications have shown some limitations, cooperative recurrent neural network approaches have been developed to overcome drawbacks of these single recurrent neural networks. This paper surveys in details work on cooperative recurrent neural networks for solving constrained optimization problems and their engineering applications, and points out their standing models from viewpoint of both convergence to the optimal solution and model complexity. We provide examples and comparisons to shown advantages of these models in the given applications.

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“…The use of multiple neural systems was described in the work of Sharkey (1998). A recent review on neural network ensembles can be found in (Yang and Browne 2001).…”

Section: Ensemble Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cooperative recurrent modular neural networks for constrained optimization: a survey of models and applications

Kamel

Xia

2008

Cogn Neurodyn

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“…Ensembles can reduce the variance of estimation errors and improve the overall classification accuracy. Many ensemble-based approaches have been proposed in recent research, including an ANN ensemble for decision support system (Ohlsson, 2004), an ensemble of ANNs for breast cancer and liver disorder prediction (Yang & Browne, 2004), MDSS with an ensemble of several different classifiers for breast diagnosis (West, Mangiameli, Rampal, & West, 2005), and multiple classifier combinations with an evolutionary approach (Kim, Min, & Han, 2006). …”

Section: Related Workmentioning

confidence: 99%

AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction

Eom

Kim²,

Zhang

2008

Expert Systems with Applications

118

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Conventional clinical decision support systems are generally based on a single classifier or a simple combination of these models, showing moderate performance. In this paper, we propose a classifier ensemble-based method for supporting the diagnosis of cardiovascular disease (CVD) based on aptamer chips. This AptaCDSS-E system overcomes conventional performance limitations by utilizing ensembles of different classifiers. Recent surveys show that CVD is one of the leading causes of death and that significant life savings can be achieved if precise diagnosis can be made. For CVD diagnosis, our system combines a set of four different classifiers with ensembles. Support vector machines and neural networks are adopted as base classifiers. Decision trees and Bayesian networks are also adopted to augment the system. Four aptamer-based biochip data sets including CVD data containing 66 samples were used to train and test the system. Three other supplementary data sets are used to alleviate data insufficiency. We investigated the effectiveness of the ensemble-based system with several different aggregation approaches by comparing the results with single classifier-based models. The prediction performance of the AptaCDSS-E system was assessed with a cross-validation test. The experimental results show that our system achieves high diagnosis accuracy (>94%) and comparably small prediction difference intervals (<6%), proving its usefulness in the clinical decision process of disease diagnosis. Additionally, 10 possible biomarkers are found for further investigation.

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“…That is, neural networks have the ability to provide flexible mapping between inputs and outputs. Secondly, neural networks are far from being optimal classifier [21]. Many experimental results have shown the generalization of individual networks is not unique.…”

Section: Introductionmentioning

confidence: 99%

Credit Risk Analysis Using a Reliability-Based Neural Network Ensemble Model

Lai

Wang

et al. 2006

Lecture Notes in Computer Science

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Abstract. Credit risk analysis is an important topic in the financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. An accurate estimation of credit risk could be transformed into a more efficient use of economic capital. In this study, we try to use a triple-phase neural network ensemble technique to design a credit risk evaluation system to discriminate good creditors from bad ones. In this model, many diverse neural network models are first created. Then an uncorrelation maximization algorithm is used to select the appropriate ensemble members. Finally, a reliability-based method is used for neural network ensemble. For further illustration, a publicly credit dataset is used to test the effectiveness of the proposed neural ensemble model.

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Neural network ensembles: combining multiple models for enhanced performance using a multistage approach

Cited by 72 publications

References 27 publications

Cooperative recurrent modular neural networks for constrained optimization: a survey of models and applications

Cooperative recurrent modular neural networks for constrained optimization: a survey of models and applications

AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction

Credit Risk Analysis Using a Reliability-Based Neural Network Ensemble Model

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