k-maxitive fuzzy measures: a scalable approach to model interactions

International audienceFuzzy measures are powerful at modeling interactions between elements. Unfortunately, they use a number of coefficients that exponentially grows with the number of elements. Beyond the computational complexity, assigning a value to any coalition of a large set of elements does not make sense. k-order measures model interactions involving at most k elements. The number of coefficients to identify is reduced and their modeling capacity is preserved in real problems where the number of interacting elements is limited. In extreme situations of full redundancy or complementariness, it is mathematically proven that the complete fuzzy measure is both k-additive and k-maxitive. A learning algorithm to identify k-maxitive measures from labeled data is designed on the basis of HLMS (Heuristic Least Mean Squares). In a classification context, the study of synthetic data with partial redundancy or complementariness supports the idea that the difference between full and partial interaction is a matter of degree, not of kind. Dealing with two real world datasets, a comparison of the complete fuzzy measure and a k-maxitive one shows the number of interacting elements is limited and the k-maxitive measures yield the same characterization of interactions and a comparable classification accuracy

Sélection de systèmes incluant plusieurs classifieurs

Selective multiclassifier systems are proposed for the effective combination of a given population of non specialized classifiers. Their design is based on the introduction of a greedy and error adaptive selection process, which aims to discover a good subset of cooperative classifiers for posterior combination on a parallel form. Without loss of generality, the fuzzy integral combination rule is considered. Experimental results on bench- mark UCI and real data show the feasibility of our approach.Nous proposons une méthode pour sélectionner les classifieurs suivant leur aptitude à coopérer, parmi une population de décideurs non spécialisés. La sélection est basée sur la performance de chacun des individus et sur celle de leur combinaison. La méthode est appliquée sur des jeux de données test (UCI) ainsi que sur nos propres données. Pour ce faire, l'intégrale floue a été choisie comme outil de combinaison. Toute autre technique reste utilisable

Sélection de systèmes incluant plusieurs classifieurs

Selective multiclassifier systems are proposed for the effective combination of a given population of non specialized classifiers. Their design is based on the introduction of a greedy and error adaptive selection process, which aims to discover a good subset of cooperative classifiers for posterior combination on a parallel form. Without loss of generality, the fuzzy integral combination rule is considered. Experimental results on bench- mark UCI and real data show the feasibility of our approach.Nous proposons une méthode pour sélectionner les classifieurs suivant leur aptitude à coopérer, parmi une population de décideurs non spécialisés. La sélection est basée sur la performance de chacun des individus et sur celle de leur combinaison. La méthode est appliquée sur des jeux de données test (UCI) ainsi que sur nos propres données. Pour ce faire, l'intégrale floue a été choisie comme outil de combinaison. Toute autre technique reste utilisable

Gender Related Differences in Kidney Injury Induced by Mercury

The aim of this study was to determine if there are sex-related differences in the acute kidney injury induced by HgCl<sub>2</sub> since female rats express lower levels of renal Oat1 and Oat3 (transporters involved in renal uptake of mercury) as compared with males. Control males and females and Hg-treated male and female Wistar rats were employed. Animals were treated with HgCl<sub>2</sub> (4 mg/kg body weight (b.w.), intraperitoneal (i.p.)) 18 h before the experiments. HgCl<sub>2</sub> induced renal impairment both in male and female rats. However, female rats showed a lower renal impairment than male rats. The observed increase in kidney weight/body weight ratio seen in male and female rats following HgCl<sub>2</sub> treatment was less in the female rats. Urine volume and creatinine clearance decreased and Oat5 urinary excretion increased in both males and females, but to a lesser degree in the latter. Urinary alkaline phosphatase (AP) activity and histological parameters were modified in male but not in female rats after HgCl<sub>2</sub> administration. These results indicate that the lower Oat1 and Oat3 expression in the kidney of females restricts Hg uptake into renal cells protecting them from this metal toxicity. These gender differences in renal injury induced by mercury are striking and also indicate that Oat1 and Oat3 are among the main transporters responsible for HgCl<sub>2</sub>-induced renal injury

Une approche enveloppe spectrale pour améliorer l'algorithme SVM-RFE sur des données infra rouge

International audienceInfrared spectroscopy data is characterized by the presence of a huge number of variables. Applications of infrared spectroscopy in the mid-infrared (MIR) and near-infrared (NIR) bands are of widespread use in many fields. To effectively handle this type of data, suitable dimensionality reduction methods are required. In this paper, a dimensionality reduction method designed to enable effective Support Vector Machine Recursive Feature Elimination (SVM-RFE) on NIR/MIR datasets is presented. The method exploits the information content at peaks of the spectral envelope functions which characterize NIR/MIR spectra datasets. Experimental evaluation across different NIR/MIR application domains shows that the proposed method is useful for the induction of compact and accurate SVM classifiers for qualitative NIR/MIR applications involving stringent interpretability or time processing requirements

Set characterization-selection towards classification based on interaction index

In many real world datasets both the individual and coordinated action of features may be relevant for class identification. In this paper, a computational strategy for relevant feature selection based on the characterization of redundant or complementary features is proposed. The characterization is achieved using fuzzy measures and an interaction index computed from fuzzy measure coefficients. Fuzzy measure identification requires raw data to be turned into confidence degrees. This key step is carried out considering the distributions of feature values across all the classes. Fuzzy measure coefficients are then estimated with an improved version of the Heuristic Least Mean Squares algorithm that includes an efficient management of untouched coefficients. Then, a generalization of the Shapley index for an arbitrary number of features is used. Simulations experiments on synthetic datasets are performed to study the behavior of this generalized interaction index. For extreme datasets, containing either redundant or complementary features as well as noise, the index value is defined by mathematical formula. This result is used to motivate feature selection guidelines that take into account feature interactions. Experimental results on benchmark datasets show that the proposal allows for the design of compact, interpretable and competitive classification models.Fil: Murillo, Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; ArgentinaFil: Guillaume, S.. Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture; FranciaFil: Spetale, Flavio Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; ArgentinaFil: Tapia, E.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; ArgentinaFil: Bulacio, P.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; Argentin