A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC's ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author

Combining learning methods and time-scale analysis for defect diagnosis of a tramway guiding system

International audienceThis paper presents a diagnosis system for detecting tramway rollers defects. First, the continuous wavelet transform is applied on vibra- tion signals measured by specific accelerometers. Then, the Singular Values Decomposition (SVD) is applied on the time-scale representations to extract a set of singular values as classification features. The resulting multi-class classification problem is decomposed into several 2-class sub-problems. The predicted probabilities are coupled using a pairwise coupling method. Empirical results demonstrate the efficiency and robustness of the overall diagnosis system on measurement data

Dynamical Recurrent Neural Networks - Towards Environmental Time Series Prediction

Dynamical Recurrent Neural Networks (DRNN) (Aussem 1994) are a class of fully recurrent networks obtained by modeling synapses as autoregressive filters. By virtue of their internal dynamic, these networks approximate the underlying law governing the time series by a system of nonlinear difference equations of internal variables. They therefore provide history-sensitive forecasts without having to be explicitly fed with external memory. The model is trained by a local and recursive error propagation algorithm called temporal-recurrent-backpropagation. The efficiency of the procedure benefits from the exponential decay of the gradient terms backpropagated through the adjoint network. We assess the predictive ability of the DRNN model with meteorological and astronomical time series recorded around the candidate observation sites for the future VLT telescope. The hope is that reliable environmental forecasts provided with the model will allow the modern telescopes to be preset, a few hou..

New Problems and Approaches Related to Large Databases in Astronomy

Analyzing large image and text databases poses particular computational problems. Computational problems can sometimes be solved by using traditional analysis techniques, and by throwing more and more memory cycles at them. A more aesthetic way to tackle such scalability problems is to find new data structures and new algorithms which will more thoroughly deal with these issues. One of the most looming issues in data analysis is the laborious phase prior to the main analysis: selection of data, coding, etc. We summarize some recent results in data coding. We then look at how the incorporation of the wavelet transform into data analysis can helpfully mitigate some problems related to preliminary data processing. We look at how these same principles (but with a different wavelet transform) can be used in time series prediction. 1 Preliminary Data Processing, Coding and Analysis: Overview The major part of data analysis does not go into the analysis itself, but rather into the demarcatio..

Résumé automatique multi-documents guidé par une base de résumés similaires

International audienceLe résumé multi-documents est une tâche difficile en traitement automatique du langage, ayant pour objectif de résumer les informations de plusieurs documents. Cependant, les documents sources sont souvent insuffisants pour obtenir un résumé qualitatif. Nous proposons un modèle guidé par un système de recherche d'informations combiné avec une mémoire non paramétrique pour la génération de résumés. Ce modèle récupère des candidats pertinents dans une base de données, puis génère le résumé en prenant en compte les candidats avec un mécanisme de copie et les documents sources. Cette mémoire non paramétrique est implémentée avec la recherche approximative des plus proches voisins afin de faire des recherches dans de grandes bases de données. Notre méthode est évalué sur le jeu de données MultiXScience qui regroupe des articles scientifiques. Enfin, nous discutons de nos résultats et des orientations possibles pour de futurs travaux

Unsupervised Feature Selection with Ensemble Learning

International audienceIn this paper, we show that the way internal estimates are used to measure variable importance in Random Forests are also applicable to feature selection in unsupervised learning. We propose a new method called Random Cluster Ensemble (RCE for short), that estimates the out-of-bag feature importance from an ensemble of partitions. Each partition is constructed using a different bootstrap sample and a random subset of the features. We provide empirical results on nineteen benchmark data sets indicating that RCE, boosted with a recursive feature elimination scheme (RFE), can lead to significant improvement in terms of clustering accuracy, over several state-of-the-art supervised and unsupervised algorithms, with a very limited subset of features. The method shows promise to deal with very large domains. All results, datasets and algorithms are available on line

Répondre aux requêtes des étudiants avec un agent conversationnel à mémoire supervisée

International audienceNous présentons l'agent conversationnel (neuronal) qui a été développé à l'Université Lyon 1 pour répondre aux questions des candidats au Master Data Science. Basé sur une architecture Seq2Seq [3] combinée avec une mémoire supervisée, l'agent est capable d'identifier l'intention de l'utilisateur et d'encoder les informations pertinentes des échanges passés afin de produire une réponse personnalisée. Un générateur de dialogue a permis de constituer une base d'entraînement de 7500 dialogues synthétiques pour l'apprentissage des paramètres du modèle. L'agent conversationnel a été déployé et est accessible en ligne à l'adresse suivante : http://chatbotinfo.univ-lyon1.fr/