Feature selection to enhance a two-stage evolutionary algorithm in product unit neural networks for complex classification problems

This paper combines feature selection methods with a two-stage evolutionary classifier based on product unit neural networks. The enhanced methodology has been tried out with four filters using 18 data sets that report test error rates about 20 % or above with reference classifiers such as C4.5 or 1-NN. The proposal has also been evaluated in a liver-transplantation real-world problem with serious troubles in the data distribution and classifiers get low performance. The study includes an overall empirical comparison between the models obtained with and without feature selection using different kind of neural networks, like RBF, MLP and other state-of-the-art classifiers. Statistical tests show that our proposal significantly improves the test accuracy of the previous models. The reduction percentage in the number of inputs is, on average, above 55 %, thus a greater efficiency is achieved.MICYT TIN2007-68084- C02-02MICYT TIN2008-06681-C06-03MICYT TIN2011-28956-C0

Improving the Accuracy of a Two-Stage Algorithm in Evolutionary Product Unit Neural Networks for Classification by Means of Feature Selection

This paper introduces a methodology that improves the accuracy of a two-stage algorithm in evolutionary product unit neural networks for classification tasks by means of feature selection. A couple of filters have been taken into consideration to try out the proposal. The experimentation has been carried out on seven data sets from the UCI repository that report test mean accuracy error rates about twenty percent or above with reference classifiers such as C4.5 or 1-NN. The study includes an overall empirical comparison between the models obtained with and without feature selection. Also several classifiers have been tested in order to illustrate the performance of the different filters considered. The results have been contrasted with nonparametric statistical tests and show that our proposal significantly improves the test accuracy of the previous models for the considered data sets. Moreover, the current proposal is much more efficient than a previous methodology developed by us; lastly, the reduction percentage in the number of inputs is above a fifty five, on average.MICYT TIN2007-68084-C02-02MICYT TIN2008-06681-C06-03Junta de Andalucía P08-TIC-374

Using machine learning methods to determine a typology of patients with HIV-HCV infection to be treated with antivirals

Several European countries have established criteria for prioritising initiation of treatment in patients infected with the hepatitis C virus (HCV) by grouping patients according to clinical characteristics. Based on neural network techniques, our objective was to identify those factors for HIV/HCV co-infected patients (to which clinicians have given careful consideration before treatment uptake) that have not being included among the prioritisation criteria. This study was based on the Spanish HERACLES cohort (NCT02511496) (April-September 2015, 2940 patients) and involved application of different neural network models with different basis functions (product-unit, sigmoid unit and radial basis function neural networks) for automatic classification of patients for treatment. An evolutionary algorithm was used to determine the architecture and estimate the coefficients of the model. This machine learning methodology found that radial basis neural networks provided a very simple model in terms of the number of patient characteristics to be considered by the classifier (in this case, six), returning a good overall classification accuracy of 0.767 and a minimum sensitivity (for the classification of the minority class, untreated patients) of 0.550. Finally, the area under the ROC curve was 0.802, which proved to be exceptional. The parsimony of the model makes it especially attractive, using just eight connections. The independent variable "recent PWID" is compulsory due to its importance. The simplicity of the model means that it is possible to analyse the relationship between patient characteristics and the probability of belonging to the treated group