Papers by FRANCISCO JAVIER GARCIA CASTELLANO
INTELIGENCIA ARTIFICIAL, 2001
Lecture Notes in Computer Science, 2002
SOAP (simple object access protocol) is a protocol that allows the access to remote objects indep... more 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 different transport types (for instance, it could be called as a CGI or as sockets). In this paper an approach to evolutionary distributed optimisation of multilayer perceptrons (MLP) using SOAP and language Perl has been done. Obtained results show that the parallel version of the developed programs obtains similar or better results using much less time than the sequential version, obtaining a good speedup. Also it can be shown that obtained results are better than those obtained by other authors using different methods.
Information Sciences, 2004
This paper is focused on determining the parameters of radial basis function neural networks (num... more 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 build a radial basis function neural networks (RBF NN) that solves a specified problem, in this case related to currency exchange rates forecasting. The evolutionary algorithm EvRBF has been implemented using the evolutionary computation framework evolving object, which allows direct evolution of problem solutions. Thus no internal representation is needed, and specific solution domain knowledge can be used to construct specific evolutionary operators, as well as cost or fitness functions. Results obtained are compared with existent bibliography, showing an improvement over the published methods.
This paper presents the application of a method (G-Prop) based on an evolutionary algorithm (EA) ... more This paper presents the application of a method (G-Prop) based on an evolutionary algorithm (EA) and backpropagation (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 operators, one of which is BP training. The EA works on the initial weights and structure of the MLP, which is then trained using QuickProp in order to compute its fitness; thus G-Prop combines the advantages of the global search performed by the EA over the MLP parameter space and the local search of the BP algorithm. Besides, variation operators are directly applied to the MLP object, not needing any other kind of representation. G-Prop algorithm has been tested by applying it to two commonly used function approximation problems, and compared with several methods, proving that it obtains good results on approximation ability.
Lecture Notes in Computer Science, 2005
In this paper we explore the use of several types of structural restrictions within algorithms fo... more 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 learning Bayesian networks from data can benefit from this prior knowledge to get better results. We formally define three types of restrictions: existence of arcs and/or edges, absence of arcs and/or edges, and ordering restrictions, and also study their interactions and how they can be managed within Bayesian network learning algorithms based on the score+search paradigm. Then we particularize our study to the classical local search algorithm with the operators of arc addition, arc removal and arc reversal, and carry out experiments using this algorithm on several data sets.
A Decision Support Tool for Credit Domains: Bayesian Network with a Variable Selector Based on Imprecise Probabilities
International Journal of Fuzzy Systems, 2021
A Bayesian Network (BN) is a graphical structure, with associated conditional probability tables.... more 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 be more powerful than the one obtained from classifiers. A BN can be built from data and have analytical and diagnostic capabilities that make it very suitable for credit domains. Credit scoring and risk analysis are fundamental tasks for financial institutions with the aim to avoid important losses. In these tasks and other domains, an excessive number of features can convert a BN into a complex and difficult to interpret model, but a few number of features can represent a loss of information obtained from data. A new method based on imprecise probabilities is presented to select an informative subset of features. Using this new feature selection method, we can build a BN that has an excellent adjustment to the data, considering a reduced number of features. Via a set of experiments, it is shown that the adjustment is better than the ones obtained with no previous variable selection method and with a similar and successful variable subset selection method based on precise probabilities. Finally, a BN is built with two important characteristics: (i) it represents a better adjustment to the data; and (ii) it has a low complexity (better interpretability) due to the small number of important selected features. A practical example about inference on a BN to help on credit risk analysis is also presented.
Expert Systems with Applications, 2017
In the last years, the application of artificial intelligence methods on credit risk assessment h... more 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 show that ensembles of classifiers achieve the better results for this kind of tasks. In this paper, it is extended a previous work about the selection of the best base classifier used in ensembles on credit data sets. It is shown that a very simple base classifier, based on imprecise probabilities and uncertainty measures, attains a better trade-off among some aspects of interest for this type of studies such as accuracy and area under ROC curve (AUC). The AUC measure can be considered as a more appropriate measure in this grounds, where the different type of errors have different costs or consequences. The results shown here present to this simple classifier as an interesting choice to be used as base classifier in ensembles for credit scoring and bankruptcy prediction, proving that not only the individual performance of a classifier is the key point to be selected for an ensemble scheme.
Methods to Determine the Branching Attribute in Bayesian Multinets Classifiers
Lecture Notes in Computer Science, 2005
Selective Gaussian Naïve Bayes Model for Diffuse Large-B-Cell Lymphoma Classification: Some Improvements in Preprocessing and Variable Elimination
Lecture Notes in Computer Science, 2005
... Variable Elimination Andrés Cano, Javier G. Castellano, Andrés R. Masegosa, and Serafın Moral... more ... Variable Elimination Andrés Cano, Javier G. Castellano, Andrés R. Masegosa, and Serafın Moral Dept. ... Journal of Intelligent and Fuzzy Systems 12 (2002) 2534 4. Langley, P., Iba, W., Thompson, K.: An analysis of bayesian classifiers. ...
Evolutionary Computation Visualization: Application to G-PROP
Lecture Notes in Computer Science, 2000
Genetic Algorithm Visualization Using Self-organizing Maps
Lecture Notes in Computer Science, 2002
Bayesian networks classifiers for gene-expression data
2011 11th International Conference on Intelligent Systems Design and Applications, 2011
Machine Learning, 2005
There is a commonly held opinion that the algorithms for learning unrestricted types of Bayesian ... more 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) topologies have since been developed, most of these being extensions (using augmenting arcs) or modifications of the Naive Bayes basic topology. In this paper, we present a new algorithm to induce classifiers based on Bayesian networks which obtains excellent results even when standard scoring functions are used. The method performs a simple local search in a space unlike unrestricted or augmented DAGs. Our search space consists of a type of partially directed acyclic graph (PDAG) which combines two concepts of DAG equivalence: classification equivalence and independence equivalence. The results of exhaustive experimentation indicate that the proposed method can compete with state-of-the-art algorithms for classification.
International Journal of Approximate Reasoning, 2007
The use of several types of structural restrictions within algorithms for learning Bayesian netwo... more 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 learning the structure of a Bayesian network from data can obtain better results by using this prior knowledge. Three types of restrictions are formally defined: existence of arcs and/or edges, absence of arcs and/or edges, and ordering restrictions. We analyze the possible interactions between these types of restrictions and also how the restrictions can be managed within Bayesian network learning algorithms based on both the score + search and conditional independence paradigms. Then we particularize our study to two classical learning algorithms: a local search algorithm guided by a scoring function, with the operators of arc addition, arc removal and arc reversal, and the PC algorithm. We also carry out experiments using these two algorithms on several data sets.
Si he visto más lejos es porque estoy sentado sobre los hombros de gigantes". iii por su amistad,... more Si he visto más lejos es porque estoy sentado sobre los hombros de gigantes". iii por su amistad, no puedo olvidarme de Rubén Armañanzas, Rosa Blanco, Guzmán Santafé, José Luis Flores, Aritz Pérez y al resto de becarios que por entonces pululaba el "Intelligent Systems Group". Desde lo personal, si hay hombros en los que siempre me he apoyado, incondicionalmente, son los de mis padres Maribel Castellano e Ignacio García. Lo bueno que hay en mi, es responsabilidad casi exclusiva de ellos. A mi hermano Ignacio y a mi cuñada Conchi también quiero agradecerles su apoyo pero, sobre todo, haberme regalado a esos dos sobrinos, María y Miguel, que con su alegría y ganas de vivir son el mejor remedio para espantar mis males. También quiero que mis amigos tengan un hueco: Bruno, Carlos, Víctor, Jesús, Lúcas y, como no, Yolanda. Gracias por preocuparos, por cuidarme y por ayudarme a desconectar. Pero hay un personaje, que por su compañía, del que no me puedo olvidar: es el gigante más chico y más viejo, Coco, mi perrete. Mientras estaba enfrascado en la redacción de este trabajo, era el encargado de darme con la pata cuando estaba demasiado tiempo en frente del ordenador o de sacarme de paseo para que me diera un poco el aire. Muchísimas gracias. iv 7.1. Imagen de uno de los microarrays de ADN usado por Spellman y col.
Dise��o de redes Neuronales artificiales mediante Algoritmos Evolutivos
Lamarckian evolution and the Baldwin effect in evolutionary neural networks
Entropy, 2019
Presently, there is a critical need to analyze traffic accidents in order to mitigate their terri... more 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 produced by accidents that involve inexperienced drivers in urban areas. The analysis was based on data provided by the Spanish General Traffic Directorate. The information root node variation (IRNV) method (based on decision trees) was used to get a rule set that provides useful information about the most probable causes of fatalities in accidents involving inexperienced drivers in urban areas. This may prove useful knowledge in preventing this kind of accidents and/or mitigating their consequences.
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Papers by FRANCISCO JAVIER GARCIA CASTELLANO