2024-03-29T12:12:50Zhttps://www.tdx.cat/oai/requestoai:www.tdx.cat:10803/486382017-09-14T21:55:29Zcom_10803_480col_10803_387219
nam a 5i 4500
Ensemble
Neural Networks
Multilayer Feedforward
Mixture
Stacked
Combination
Multiple Classifier Systems
Cross-Validation
Boosting
Reordering
Ensembles of Artificial Neural Networks: Analysis and Development of Design Methods
[Castelló] :
Universitat Jaume I,
2011
Accés lliure
http://hdl.handle.net/10803/48638
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AAMMDDs2011 sp ||||fsm||||0|| 0 eng|c
978-84-695-1324-8
Torres Sospedra, Joaquín,
autor
1 recurs en línia (415 pàgines)
Tesi
Doctorat
Universitat Jaume I. Departament d'Enginyeria i Ciència dels Computadors
2011
Universitat Jaume I. Departament d'Enginyeria i Ciència dels Computadors
Tesis i dissertacions electròniques
Hernández Espinosa, Carlos,
supervisor acadèmic
Fernández Redondo, Mercedes,
supervisor acadèmic
TDX
<p>This thesis is focused on the analysis and development of Ensembles of Neural Networks. An ensemble is a system in which a set of heterogeneous Artificial Neural Networks are generated in order to outperform the Single network based classifiers. However, this proposed thesis differs from others related to ensembles of neural networks [1, 2, 3, 4, 5, 6, 7] since it is organized as follows.
<p>In this thesis, firstly, an ensemble methods comparison has been introduced in order to provide a rank-based list of the best ensemble methods existing in the bibliography. This comparison has been split into two researches which represents two chapters of the thesis.
<p>Moreover, there is another important step related to the ensembles of neural networks which is how to combine the information provided by the neural networks in the ensemble. In the bibliography, there are some alternatives to apply in order to get an accurate combination of the information provided by the heterogeneous set of networks. For this reason, a combiner comparison has also been introduced in this thesis.
<p>Furthermore, Ensembles of Neural Networks is only a kind of Multiple Classifier System based on neural networks. However, there are other alternatives to generate MCS based on neural networks which are quite different to Ensembles. The most important systems are Stacked Generalization and Mixture of Experts. These two systems will be also analysed in this thesis and new alternatives are proposed.
<p>One of the results of the comparative research developed is a deep understanding of the field of ensembles. So new ensemble methods and combiners can be designed after analyzing the results provided by the research performed. Concretely, two new ensemble methods, a new ensemble methodology called Cross-Validated Boosting and two reordering algorithms are proposed in this thesis. The best overall results are obtained by the ensemble methods proposed.
<p>Finally, all the experiments done have been carried out on a common experimental setup. The experiments have been repeated ten times on nineteen different datasets from the UCI repository in order to validate the results. Moreover, the procedure applied to set up specific parameters is quite similar in all the experiments performed.
<p>It is important to conclude by remarking that the main contributions are:
<p>1) An experimental setup to prepare the experiments which can be applied for further comparisons.
2) A guide to select the most appropriate methods to build and combine ensembles and multiple classifiers systems.
3) New methods proposed to build ensembles and other multiple classifier systems.
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