Liste de nos séminaires

La classification évidentielle et non supervisée se caractérise par l'utilisation de fonctions de croyance, et notamment l'utilisation de la notion de partition crédale. Cette notion élargit le concept de partition nette, floue, probabiliste ou possibiliste. Ainsi, elle permet de mesurer de manière précise l'incertitude quant à l'affectation d'un objet à une classe.

La classification sous contraintes, également appelée classification semi-supervisée, est une approche qui introduit une connaissance a priori sous forme de contraintes sur la partition recherchée. Nous nous intéressons ici à des contraintes au niveau des objets : une contrainte Must-Link spécifie que deux objets doivent être dans la même classe alors qu'une contrainte Cannot-link indique que deux objets se trouvent dans des classes différentes. L'ajout de contraintes permet une amélioration sensible des résultats de classification. Néanmoins, dans le cadre d'applications réelles, il est parfois difficile d'obtenir un jeu de contraintes intéressant. L'apprentissage actif consiste donc à obtenir ces informations à moindre coût.

Dans cette présentation, nous proposons deux nouveaux algorithmes de classification sous contraintes utilisant le cadre théorique des fonctions de croyance. Grâce à la partition crédale qu'ils retournent, nous pouvons identifier de manière précise les objets problématiques pour la classification. Un nouvel algorithme d'apprentissage actif est alors proposé afin de réduire l'erreur de classification.

A long-term effort toward a general application framework
for intelligent systems is introduced. Many intelligent systems
adopt a knowledge-based system architecture, and their development
thus differs from other application development. Expressing knowledge
as rules shifts one's perspective from data manipulation to relation
investigation. Our recent progress about two components are focused
- Extended Logic Programming (ELP), i.e. the keystone of this framework,
and a multi-view visualization scheme in order to effectively and
efficiently visualize the reasoning processes of ELP. Few representative
applications are showcased as time allows.

Reference:

K. Springer, M. Henry, A. Inoue, "A General Application Framework for Intelligent Systems,"
The 20th Midwest Artificial Intelligence and Cognitive Science Conference (MAICS2009),
Fort Wayne, IN, pp. 188-195, 2009.

This talk is about our recent work in the area of feature weighting for high dimensional data classification (and clustering). The first part of my talk relates to Naive Bayes (NB for short) classifier. Currently, in many real-world applications, high-dimensionality poses a major challenge to conventional NB classifiers, due to noisy or redundant features and local relevance of these features to classes. In this work, we propose an automated feature weighting solution to enable the NB method to deal effectively with high-dimensional data. First a locally weighted probability model will be presented for implementing a soft feature selection scheme. Then an optimization algorithm will be presented to find the weights in linear time complexity, based on the Logitnormal priori distribution and the Maximum a Posteriori principle. Experimental studies will show the effectiveness and suitability of the proposed model for high-dimensional data classification.

In the second part of this talk, I will briefly present our work on central clustering of categorical data with automated feature weighting. A novel kernel-density-based definition of cluster center is proposed using a Bayes-type probability estimator. Then, an algorithm called k-centers is proposed incorporating a new feature weighting scheme by which each attribute is automatically assigned with a weight measuring its individual contribution for the clusters.

The web has evolved from a technology platform to a social milieu where a mix of factual, opinion and behavior data interleave. A number of social applications are being built to analyze and extract value from this data and is encouraging us to do data-driven research.
I will describe a perspective on why and how social data management is fundamentally different from data management as it is taught in school today. More specifically, I'll talk about social data preparation, social data exploration and social application validation.
This talk is based on published and ongoing work with colleagues at LIG, UT Austin, U. of Trento, U. of Tacoma, and Google Research.

Recommender systems aim to predict the content that a user would like based on observations of the online behaviour of its users. Research in the CWI Information Access group addresses different aspects of this problem, varying from how to measure recommendation results, how recommender systems relate to information retrieval models, and how to build effective recommender systems (note: we won the ACM RecSys 2013 News Recommender Systems challenge!). We would like to develop a general methodology to diagnose weaknesses and strengths of recommender systems. In this talk, I discuss the initial results of an analysis of the core component of collaborative filtering recommenders: the similarity metric used to find the most similar users (neighbours) that will provide the basis for the recommendation to be made. The purpose is to shed light on the question why certain user similarity metrics have been found to perform better than others. We have studied statistics computed over the distance distribution in the neighbourhood as well as properties of the nearest neighbour graph. The features identified correlate strongly with measured prediction performance - however, we have not yet discovered how to deploy this knowledge to actually improve recommendations made.