Arnaud Quirin

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Different tasks in forensics require the use of 3D models of forensic objects (skulls, bones, corpses, etc.) captured by 3D range scanners. Since a whole object cannot be completely scanned in a single image using a range scanner, multiple acquisitions from different views are needed to supply the information to construct the 3D model by a range image(More)
Scientograms are a kind of graph representations depicting the state of Science in a specific domain. The automatic comparison and analysis of a set of scientograms, to show for instance the evolution of a scientific domain of a given country, is an interesting but challenging task as the handled data is huge and complex. In this paper, we aim to show that(More)
In [14] we proposed a scheme to generate fuzzy rule-based multiclassification systems by means of bagging, mutual information-based feature selection, and a multicriteria genetic algorithm for static component classifier selection guided by the ensemble training error. In the current contribution we extend the latter component by making use of the bagging(More)
In the last few years, there is an increasing interest to generate visual representations of very large scientific domains. A methodology based on the combined use of ISI–JCR category cocitation and social networks analysis through the use of the Pathfinder algorithm has demonstrated its ability to achieve high quality, schematic visualizations for these(More)
0020-0255/$ see front matter 2009 Elsevier Inc doi:10.1016/j.ins.2009.11.007 * Corresponding author. Tel.: +34 868 887882. E-mail addresses: emilioserra@um.es (E. Serrano) (O. Cordón). This paper introduces a newmethodology based on the use of Pathfinder networks (PFNETs) for the debugging of multi-agent systems (MASs). This methodology is specifically(More)
Since Zadeh’s proposal and Mamdani’s seminal ideas, interpretability is acknowledged as one of the most appreciated and valuable characteristics of fuzzy system identification methodologies. It represents the ability of fuzzy systems to formalize the behavior of a real system in a human understandable way, by means of a set of linguistic variables and rules(More)
Fuzzy rule-based classification systems (FRBCSs) are able to design interpretable classifiers but suffer from the curse of dimensionality when dealing with complex problems with a large number of features. In this contribution we explore the use of popular approaches for designing ensembles of classifiers in the machine learning field, bagging and random(More)
Subgrap h mining is the process of identifying concep ts describing interesting and repetitive subgraphs within graph-based data. The exponential number of possible subgraphs makes the problem very challenging. Existing methods apply a single-objective subgraph search with the view that interesting subgraphs are those capable of not merely compressing the(More)
In this contribution we explore the combination of bagging with random subspace and two variants of Battiti's mutual information feature selection methods to design fuzzy rule-based classification system ensembles. Besides, we consider a multicriteria genetic algorithm guided by the training error to select the component classifiers, in order to look for(More)