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Filter-based feature selection in the context of evolutionary neural networks in supervised machine learning

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2019-03-11
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Springer
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This paper presents a workbench to get simple neural classification models based on product evolutionary networks via a prior data preparation at attribute level by means of filter-based feature selection. Therefore, the computation to build the classifier is shorter, compared to a full model without data pre-processing, which is of utmost importance since the evolutionary neural models are stochastic and different classifiers with different seeds are required to get reliable results. Feature selection is one of the most common techniques for pre-processing the data within any kind of learning task. Six filters have been tested to assess the proposal. Fourteen (binary and multi-class) difficult classification data sets from the University of California repository at Irvine have been established as the test bed. An empirical study between the evolutionary neural network models obtained with and without feature selection has been included. The results have been contrasted with nonparametric statistical tests and show that the current proposal improves the test accuracy of the previous models significantly. Moreover, the current proposal is much more efficient than the previous methodology; the time reduction percentage is above 40%, on average. Our approach has also been compared with several classifiers both with and without feature selection in order to illustrate the performance of the different filters considered. Lastly, a statistical analysis for each feature selector has been performed providing a pairwise comparison between machine learning algorithms.
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-88209-C2-1-R/ES/BIG DATA STREAMING: ANALISIS DE DATOS MASIVOS CONTINUOS. MODELOS PREDICTIVOS/
info:eu-repo/grantAgreement/MICINN//TIN2011-28956-C02-02/ES/ANALISIS INTELIGENTE DE INFORMACION MEDIOAMBIENTAL/
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Pattern Analysis and Applications, vol 23, nº 1, p. 467-491
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