dc.creator | Tallón Ballesteros, Antonio Javier | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.creator | Ruiz Sánchez, Roberto | es |
dc.date.accessioned | 2023-05-09T09:58:53Z | |
dc.date.available | 2023-05-09T09:58:53Z | |
dc.date.issued | 2019-08-11 | |
dc.identifier.citation | Tallón Ballesteros, A.J., Riquelme Santos, J.C. y Ruiz Sánchez, R. (2019). Semi-wrapper feature subset selector for feed-forward neural networks: Applications to binary and multi-class classification problems. Neurocomputing, 353, 28-44. https://doi.org/10.1016/j.neucom.2018.05.133. | |
dc.identifier.issn | 0925-2312 (impreso) | es |
dc.identifier.issn | 1872-8286 (online) | es |
dc.identifier.uri | https://hdl.handle.net/11441/145681 | |
dc.description.abstract | This paper explores widely the data preparation stage within the process of knowledge discovery and data mining via feature subset selection in the context of two very well-known neural models: radial basis function neural networks and multi-layer perceptron. It is known the best performance of wrapper attribute selection methods based on the evaluation measure provided by a classifier, although the temporal complexity of learning neural networks practically precludes the use of wrapper techniques, especially in complex databases with high dimensionality and a large number of labels. In this paper, we propose the use of the Naïve Bayes classifier as a fitness function within a semi-wrapper feature selec tion approach. The Naïve Bayes classifier is a good fast approach to a neural network and utilising it as a measure of goodness in a backward search on a ranking provides a specific attribute selection method for neural networks in complex data. The test-bed consists of 34 binary and multi-class classification problems and 7 feature selectors. Of these, there are 6 data sets with upwards of 5 classes. According to the reported accuracy results that have been supported by non-parametric statistical tests in different scenarios, our method has been shown to be very suitable for both kinds of neural networks. Moreover, the reduced feature-space is around 20% of the full attribute space. The speedup with the aforementioned semi-wrapper is very outstanding and its value fluctuates, on average, from about 1.5 with radial basis function neural networks to around 30 with multi-layer perceptron | es |
dc.description.sponsorship | Comisión Interministerial de Ciencia y Tecnología TIN2014-55894-C2-R | es |
dc.format | application/pdf | es |
dc.format.extent | 17 | es |
dc.language.iso | eng | es |
dc.publisher | ScienceDirect | es |
dc.relation.ispartof | Neurocomputing, 353, 28-44. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Feed-forward artificial neural networks | es |
dc.subject | Feature selection | es |
dc.subject | Supervised machine learning | es |
dc.subject | Feature subset selection | es |
dc.subject | Computational intelligence | es |
dc.subject | Semi-wrapper | es |
dc.title | Semi-wrapper feature subset selector for feed-forward neural networks: Applications to binary and multi-class classification problems | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2014-55894-C2-R | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0925231219303303?via%3Dihub | es |
dc.identifier.doi | 10.1016/j.neucom.2018.05.133 | es |
dc.journaltitle | Neurocomputing | es |
dc.publication.volumen | 353 | es |
dc.publication.initialPage | 28 | es |
dc.publication.endPage | 44 | es |
dc.contributor.funder | Comisión Interministerial de Ciencia y Tecnología (CICYT). España | es |