Actively Searching for an Effective Neural Network Ensemble

“…The simplest approach is a greedy one [104], where a new learner is added to the ensemble only if the resulting squared error is reduced, but in principle any optimization technique can be used to select the "best" component of the ensemble, including genetic algorithms [97].…”

Section: Mixtures Of Experts Methodsmentioning

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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“…The principle of the NNE has been described in detail in [9,13,14]. Having obtained each refined component FNN, we would concentrate on how to combine the output of each component FNN.…”

Section: Optimization By Multi-population Cooperative Algorithmmentioning

confidence: 99%

Network Modeling and Assessment of Ecosystem Health by a Multi-Population Swarm Optimized Neural Network Ensemble

et al. 2016

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Society is more and more interested in developing mathematical models to assess and forecast the environmental and biological health conditions of our planet. However, most existing models cannot determine the long-range impacts of potential policies without considering the complex global factors and their cross effects in biological systems. In this paper, the Markov property and Neural Network Ensemble (NNE) are utilized to construct an estimated matrix that combines the interaction of the different local factors. With such an estimation matrix, we could obtain estimated variables that could reflect the global influence. The ensemble weights are trained by multiple population algorithms. Our prediction could fit the real trend of the two predicted measures, namely Morbidity Rate and Gross Domestic Product (GDP). It could be an effective method of reflecting the relationship between input factors and predicted measures of the health of ecosystems. The method can perform a sensitivity analysis, which could help determine the critical factors that could be adjusted to move the ecosystem in a sustainable direction.

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Actively Searching for an Effective Neural Network Ensemble

Cited by 284 publications

References 13 publications

Artificial neural network ensembles in time series forecasting: an application of rainfall forecasting in Sri Lanka

Artificial neural network ensembles in time series forecasting: an application of rainfall forecasting in Sri Lanka

Ensembles of Learning Machines

Network Modeling and Assessment of Ecosystem Health by a Multi-Population Swarm Optimized Neural Network Ensemble

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