Francisco Javier García Castellano

• University of Granada

Multi-Label Classification (MLC) assumes that each instance belongs to a set of labels, unlike traditional classification, where each instance corresponds to a unique value of a class variable. Calibrated Label Ranking (CLR) is an MLC algorithm that determines a ranking of labels for a given instance by considering a binary classifier for each pair...

In Multi-Label Classification (MLC), Classifier Chains (CC) are considered simple and effective methods to exploit correlations between labels. A CC considers a binary classifier per label, in which the previous labels, according to an established order, are used as additional features. The label order strongly influences the performance of the CC,...

The Naive Credal Classifier (NCC) was the first method proposed for Imprecise Classification. It starts from the known Naive Bayes algorithm (NB), which assumes that the attributes are independent given the class variable. Despite this unrealistic assumption, NB and NCC have been successfully used in practical applications. In this work, we propose...

A Bayesian Network (BN) is a graphical structure, with associated conditional probability tables. This structure allows us to obtain different knowledge than the one obtained from standard classifiers. With a BN, representing a dataset, we can calculate different probabilities about a set of features with respect to other ones. This inference can b...

Within the field of supervised classification, the naïve Bayes (NB) classifier is a very simple and fast classification method that obtains good results, being even comparable with much more complex models. It has been proved that the NB model is strongly dependent on the estimation of conditional probabilities. In the literature, it had been shown...

In many situations, classifiers predict a set of states of a class variable because there is no information enough to point only one state. In the data mining area, this task is known as Imprecise Classification. Decision Trees that use imprecise probabilities, also known as Credal Decision Trees (CDTs), have been adapted to this field. The adaptat...

Decision Trees (DTs) have been adapted to Multi-Label Classification (MLC). These adaptations are known as Multi-Label Decision Trees (ML-DT). In this research, a new ML-DT based on the Nonparametric Predictive Inference Model on Multinomial data (NPI-M) is proposed. The NPI-M is an imprecise probabilities model that provides good results when it i...

Random forest (RF) is an ensemble learning method, and it is considered a reference due to its excellent performance. Several improvements in RF have been published. A kind of improvement for the RF algorithm is based on the use of multivariate decision trees with local optimization process (oblique RF). Another type of improvement is to provide ad...

The Credal Decision Trees (CDT) have been adapted for Imprecise Classification (ICDT). However, no ensembles of imprecise classifiers have been proposed so far. The reason might be that it is not a trivial question to combine the predictions made by multiple imprecise classifier. In fact, if the combination method used is not appropriate, the ensem...

Gene Regulatory Networks (GRNs) are known as the most adequate instrument to provide a clear insight and understanding of the cellular systems. One of the most successful techniques to reconstruct GRNs using gene expression data is Bayesian networks (BN) which have proven to be an ideal approach for heterogeneous data integration in the learning pr...

Presently, there is a critical need to analyze traffic accidents in order to mitigate their terrible economic and human impact. Most accidents occur in urban areas. Furthermore, driving experience has an important effect on accident analysis, since inexperienced drivers are more likely to suffer fatal injuries. This work studies the injury severity...

In this work, we have considered the ensemble of classifier chains (ECC) algorithm in order to solve the multi-label classification (MLC) task. It starts from binary relevance algorithm (BR), a simple and direct approach to MLC that has been shown to provide good results in practice. Nevertheless, unlike BR, ECC aims to exploit the correlations bet...

Binary Relevance (BR) is a simple and direct approach to the Multi-Label Classification (MLC). It decomposes the multi-label problem into several binary problems, one per label. It uses an algorithm of traditional supervised classification in order to solve these binary problems. On the other hand, Credal C4.5 (CC4.5) is a modification of the class...

The knowledge extraction from data with noise or outliers is a complex problem in the data mining area. Normally, it is not easy to eliminate those problematic instances. To obtain information from this type of data, robust classifiers are the best option to use. One of them is the application of bagging scheme on weak single classifiers. The Creda...

Random Forest (RF) learning algorithm is considered a classifier of reference due its excellent performance. Its success is based on the diversity of rules generated from decision trees that are built via a procedure that randomizes instances and features. To find additional procedures for increasing the diversity of the trees is an interesting tas...

The application of classifiers on data represents an important help in a process of decision making. Any classifier, or other method used for knowledge extraction, suffers a deterioration when it is applied on data with noise. Credal C4.5 (CC4.5) is a recent method of classification, that introduces imprecise probabilities in the algorithm of the c...

The Random Forest classifier has been considered as an important reference in the data mining area. The building procedure of its base classifier (a decision tree) is principally based on a randomization process of data and features; and on a split criterion, which uses classic precise probabilities, to quantify the gain of information. One drawbac...

Variable selection methods play an important role in the field of attribute mining. The Naive Bayes (NB) classifier is a very simple and popular classification method that yields good results in a short processing time. Hence, it is a very appropriate classifier for very large datasets. The method has a high dependence on the relationships between...

Data analysis techniques can be applied to discover important relations among features. This is the main objective of the Information Root Node Variation (IRNV) technique, a new method to extract knowledge from data via decision trees. The decision trees used by the original method were built using classic split criteria. The performance of new spl...

In the last years, the application of artificial intelligence methods on credit risk assessment has meant an improvement over classic methods. Small improvements in the systems about credit scoring and bankruptcy prediction can suppose great profits. Then, any improvement represents a high interest to banks and financial institutions. Recent works...

Credal Decision Trees (CDTs) are algorithms to design classifiers based on imprecise probabilities and uncertainty based information measures. These algorithms are suitable when noisy data sets are classified. This fact is explained in this paper in terms of the split criterion used in the new procedure of a special type of CDT, called Credal-C4.5....

The main aim of this study is focused on the extraction or obtaining of important decision rules (DRs) using decision trees (DTs) from traffic accidents’ data. These decision rules identify patterns related with the severity of the accident. In this work, we have incorporated a new split criterion to built decision trees in a method named Informati...

In this work, we study the application of Bayesian networks classifiers for gene expression data in three ways: first, we made an exhaustive state-of-art of Bayesian classifiers and Bayesian classifiers induced from microarray data. Second, we propose a preprocessing scheme for gene expression data, to induce Bayesian classifiers. Third, we evaluat...

En esta memoria se lleva a cabo un estudio de las distintas propuestas realizadas por otros autores en redes bayesianas, Haciendo una revisión exhaustiva de la aplicación de redes bayesianas en el tratamiento de datos de expresión genética. Se presenta una metodología para incorporar conocimiento experto en al aprendizaje automático de redes bayesi...

Software visualization is an area of computer science devoted to supporting the understanding and effective use of algorithms. The application of software visualization to Evolutionary Computation has been receiving increasing attention during the last few years. In this paper we apply visualization technique to an evolutionary algorithm for multil...

The use of several types of structural restrictions within algorithms for learning Bayesian networks is considered. These restrictions may codify expert knowledge in a given domain, in such a way that a Bayesian network representing this domain should satisfy them. The main goal of this paper is to study whether the algorithms for automatically lea...

Resumen— Hybrid neuro-evolutionary algorithms may be inspired on Darwinian or Lamarckian evolu- tion. In the case of Darwinian evolution, the Baldwin effect, that is, the progressive incorporation of learned characteristics to the genotypes, can be observed and leveraged to improve the search. The purpose of this paper is to carry out an exper- ime...

In this paper we explore the use of several types of structural restrictions within algorithms for learning Bayesian networks. These restrictions may codify expert knowledge in a given domain, in such a way that a Bayesian network representing this domain should satisfy them. Our objective is to study whether the algorithms for automatically learni...

In this work, we present some significant improvements for for feature selection in wrapper methods. They are two: the first of them consists in a proper preordering of the feature set; and the second one consists in the application of an irrelevant feature elimination method, where the irrelevance condition is subjected to the partial selected fea...

There is a commonly held opinion that the algorithms for learning unrestricted types of Bayesian networks, especially those based on the score+search paradigm, are not suitable for building competitive Bayesian network-based classifiers. Several specialized algorithms that carry out the search into different types of directed acyclic graph (DAG) to...

This paper is focused on determining the parameters of radial basis function neural networks (number of neurons, and their respective centers and radii) automatically. While this task is often done by hand, or based in hillclimbing methods which are highly dependent on initial values, in this work, evolutionary algorithms are used to automatically...

SOAP (simple object access protocol) is a protocol that allows the access to remote objects independently of the computer architecture and the language. A client using SOAP can send or receive objects, or access remote object methods. Unlike other remote procedure call methods, like XML-RPC or RMI, SOAP can use many difierent transport types (for i...

This paper gives an overview of evolutionary computation visualization and describes the application of visualization to some well known multidimensional problems. Self-Organizing Maps (SOM) are used for multidimensional scaling and projection. We show how different ways of training the SOM make it more or less adequate for the visualization task.

A pesar de que se han ideado una gran cantidad de algoritmos para entrenar los pesos de una red neuronal a través de la presentación de ejemplos para una topología fija, estos algoritmos tienen el problema de que suelen caer en óptimos locales. La obtención de buenos resultados depende en gran medida de los parámetros de aprendizaje y de los pesos...

This paper presents the application of a method (G-Prop) based on an evolutionary algorithm (EA) and backprop- agation (BP) to solve function approximation problems. The EA selects the multilayer perceptron (MLP) initial weights and learning rate, and changes the number of neurons in the hidden layer through the application of specific variation op...

Software visualization is an area of computer science devoted to supporting the understanding and effective use of algorithms. The application of software visualization to Evolutionary Computation has been receiving increasing attention during the last few years. In this paper we apply visualization technique to an evolutionary algorithm for multil...

Bayesian multinets are a Bayesian networks extension where context-specific conditional independences can be represented. The main aim of this work is to study different methods to choose the distinguished attribute in Bayesian multinets when we use them in supervised classification tasks. We have used different approaches: a wrapper method and sev...