Supervised data transformation and dimensionality reduction with a 3-layer multi-layer perceptron for classification problems

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dc.contributor.author Valls, José M.
dc.contributor.author Aler, Ricardo
dc.contributor.author Galván, Inés M.
dc.contributor.author Camacho Fernández, David
dc.date.accessioned 2021-04-23T08:25:09Z
dc.date.available 2022-01-04T00:00:04Z
dc.date.issued 2021-01-04
dc.identifier.bibliographicCitation Valls, J.M., Aler, R., Galván, I.M. et al. Supervised data transformation and dimensionality reduction with a 3-layer multi-layer perceptron for classification problems. J Ambient Intell Human Comput (2021).
dc.identifier.issn 1868-5137
dc.identifier.uri http://hdl.handle.net/10016/32461
dc.description.abstract The aim of data transformation is to transform the original feature space of data into another space with better properties. This is typically combined with dimensionality reduction, so that the dimensionality of the transformed space is smaller. A widely used method for data transformation and dimensionality reduction is Principal Component Analysis (PCA). PCA finds a subspace that explains most of the data variance. While the new PCA feature space has interesting properties, such as removing linear correlation, PCA is an unsupervised method. Therefore, there is no guarantee that the PCA feature space will be the most appropriate for supervised tasks, such as classification or regression. On the other hand, 3-layer Multi Layer Perceptrons (MLP), which are supervised methods, can also be understood as a data transformation carried out by the hidden layer, followed by a classification/regression operation performed by the output layer. Given that the hidden layer is obtained after a supervised training process, it can be considered that it is performing a supervised data transformation. And if the number of hidden neurons is smaller than the input, also dimensionality reduction. Despite this kind of transformation being widely available (any neural network package that allows access to the hidden layer weights can be used), no extensive experimentation on the quality of 3-layer MLP data transformation has been carried out. The aim of this article is to carry out this research for classification problems. Results show that, overall, this transformation offers better results than the PCA unsupervised transformation method.
dc.description.sponsorship This work has been supported by Agencia Estatal de Investigación (PID2019-107455RB-C22 /AEI/ 10.13039/501100011033), and Spanish Ministry of Science and Education under TIN2017-85727-C4-3-P (DeepBio) grant
dc.language.iso eng
dc.publisher Springer Nature
dc.rights Copyright © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature
dc.title Supervised data transformation and dimensionality reduction with a 3-layer multi-layer perceptron for classification problems
dc.type article
dc.subject.eciencia Informática
dc.identifier.doi https://doi.org/10.1007/s12652-020-02841-y
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TIN2017-87548-C4-3-P
dc.relation.projectID Gobierno de España. PID2019-107455RB-C22 /AEI
dc.type.version acceptedVersion
dc.identifier.publicationtitle Journal of Ambient Intelligence and Humanized Computing
dc.identifier.uxxi AR/0000027435
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