TALLÓN BALLESTEROS , Antonio Javier

Este artículo describe la experiencia desarrollada en la Universidad de Sevilla en la asignatura "Sistemas Operativos", inscrita en los estudios de Grado en Ingeniería Informática, con un grupo cuya docencia es impartida completamente en inglés. El período de estudio comprende desde el curso 2011/2012 hasta 2013/2014. Realizamos un análisis...

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This thesis introduces three contributions to train feed-forward neural network models based on evolutionary computation for a classification task. The new methodologies have been evaluated in three-layered neural models, including one input, one hidden and one output layer. Particularly, two kind of neurons such as product and sigmoidal units...

This paper presents a procedure to add broader diversity at the beginning of the evolutionary process. It consists of creating two initial populations with different parameter settings, evolving them for a small number of generations, selecting the best individuals from each population in the same proportion and combining them to constitute a...

This paper presents a novel procedure to apply in a sequential way two data preparation techniques from a different nature such as data cleansing and feature selection. For the former we have experienced with a partial removal of outliers via inter-quartile range whereas for the latter we have chosen relevant attributes with two widespread...

This paper introduces the use of an ant colony optimization (ACO) algorithm, called Ant System, as a search method in two wellknown feature subset selection methods based on correlation or consistency measures such as CFS (Correlation-based Feature Selection) and CNS (Consistency-based Feature Selection). ACO guides the search using a heuristic...

This paper addresses the situation that may happen after the application of feature subset selection in terms of a reduced number of selected features or even same solutions obtained by different algorithms. The data mining community has been working for a long time with the assumption that meaningful attributes are either highly correlated...

This paper introduces two statistical outlier detection approaches by classes. Experiments on binary and multi-class classification problems reveal that the partial removal of outliers improves significantly one or two performance measures for C4.S and I-nearest neighbour classifiers. Also, a taxonomy of problems according to the amount of...

A framework that combines feature selection with evolution ary artificial neural networks is presented. This paper copes with neural networks that are applied in classification tasks. In machine learning area, feature selection is one of the most common techniques for pre processing the data. A set of filters have been taken into...

This paper presents a quality enhancement of the selected features by a hybrid filter-based jointly on feature ranking and feature subset selection (FR-FSS) using a consistency-based measure via merging new features which are obtained applying other FR-FSS evaluated with a correlation metric. The goal is to overcome the accuracy of a neural...

This paper presents a workbench to get simple neural classifcation models based on product evolutionary networks via a prior data preparation at attribute level by means of flter-based feature selection. Therefore, the computation to build the classifer is shorter, compared to a full model without data pre-processing, which is of utmost...

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...

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...

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...

Digital forensics research includes several stages. Once we have collected the data the last goal is to obtain a model in order to predict the output with unseen data. We focus on supervised machine learning techniques. This chapter performs an experimental study on a forensics data task for multi-class classification including several types of...