Viewing the process of generating counterfactuals as a source of knowledge -- Application to the Naive Bayes classifier

There are now many comprehension algorithms for understanding the decisions of a machine learning algorithm. Among these are those based on the generation of counterfactual examples. This article proposes to view this generation process as a source of creating a certain amount of knowledge that can be stored to be used, later, in different ways. This process is illustrated in the additive model and, more specifically, in the case of the naive Bayes classifier, whose interesting properties for this purpose are shown.Comment: 12 page

VCNet: A self-explaining model for realistic counterfactual generation

International audienceCounterfactual explanation is a common class of methods to make local explanations of machine learning decisions. For a given instance, these methods aim to find the smallest modification of feature values that changes the predicted decision made by a machine learning model. One of the challenges of counterfactual explanation is the efficient generation of realistic counterfactuals. To address this challenge, we propose VCNet-Variational Counter Net-a model architecture that combines a predictor and a counterfactual generator that are jointly trained, for regression or classification tasks. VCNet is able to both generate predictions, and to generate counterfactual explanations without having to solve another minimisation problem. Our contribution is the generation of counterfactuals that are close to the distribution of the predicted class. This is done by learning a variational autoencoder conditionally to the output of the predictor in a join-training fashion. We present an empirical evaluation on tabular datasets and across several interpretability metrics. The results are competitive with the state-of-the-art method

Privacy in trajectory micro-data publishing : a survey

We survey the literature on the privacy of trajectory micro-data, i.e., spatiotemporal information about the mobility of individuals, whose collection is becoming increasingly simple and frequent thanks to emerging information and communication technologies. The focus of our review is on privacy-preserving data publishing (PPDP), i.e., the publication of databases of trajectory micro-data that preserve the privacy of the monitored individuals. We classify and present the literature of attacks against trajectory micro-data, as well as solutions proposed to date for protecting databases from such attacks. This paper serves as an introductory reading on a critical subject in an era of growing awareness about privacy risks connected to digital services, and provides insights into open problems and future directions for research.Comment: Accepted for publication at Transactions for Data Privac

Privacy in trajectory micro-data publishing: a survey

International audienceWe survey the literature on the privacy of trajectory micro-data, i.e., spatiotemporal information about the mobility of individuals, whose collection is becoming increasingly simple and frequent thanks to emerging information and communication technologies. The focus of our review is on privacy-preserving data publishing (PPDP), i.e., the publication of databases of trajectory micro-data that preserve the privacy of the monitored individuals. We classify and present the literature of attacks against trajectory micro-data, as well as solutions proposed to date for protecting databases from such attacks. This paper serves as an introductory reading on a critical subject in an era of growing awareness about privacy risks connected to digital services, and provides insights into open problems and future directions for research

7 : Cartes auto-organisatrices temporelles

Les cartes auto-organisatrices (SOM) sont parmi les modèles connexionnistes les plus utilisés pour la classification et la visualisation des données [KOH95] dans plusieurs domaines comme la fouille de données, le traitement de signal, les statistiques, la robotique, etc. Elles permettent de projeter des données de dimension quelconque dans un espace discret de faible dimension, ce qui facilite leur visualisation et leur interprétation. Les cartes auto-organisatrices classiques sont destinées au traitement des données statiques. Ces données sont présentées sous forme de vecteurs de dimension fixée. Cependant, dans plusieurs applications les données ne se présentent pas sous forme de vecteurs : les données séquentielles de longueurs variables ou non limitées peuvent provenir de plusieurs domaines comme les séries temporelles, les mots, les données d'usages, etc. Dans ce chapitre, nous allons décrire et comparer différents modèles de cartes auto-organisatrices qui traitent l'information temporelle de façon externe ou interne

Générer des explications contrefactuelles à l'aide d'un autoencodeur supervisé

International audienceIn this work, we investigate the problem of generating counterfactuals explanations that are both close to the data distribution, and to the distribution of the target class. Our objective is to obtain counterfactuals with likely values (i.e. realistic). We propose a method for generating realistic counterfactuals by using class prototypes. The novelty of this approach is that these class prototypes are obtained using a supervised auto-encoder. Then, we performed an empirical evaluation across several interpretability metrics, that shows competitive results with a state-of-the-art method.Dans cet article nous proposons une manière d'améliorer l'interprétabilité des explications contrefactuelles. Une explication contrefactuelle se présente sous la forme d'une version modifiée de la donnée à expliquer qui répond à la question : que faudrait-il changer pour obtenir une prédiction différente ? La solution proposée consiste à introduire dans le processus de génération du contrefactuel un terme basé sur un auto-encodeur supervisé. Ce terme contraint les explications générées à être proches de la distribution des données et de leur classe cible. La qualité des contrefactuels produits est évaluée sur un jeu de données d'images par le biais de différentes métriques. Nous montrons que notre solution s'avère compétitive par rapport à une méthode de référence de l'état de l'art

Reducing the Cold-Start Problem in Content Recommendation Through Opinion Classification

Abstract—Like search engines, recommender systems have become a tool that cannot be ignored by websites with a large selection of products, music, news or simply webpages links. The performance of this kind of system depends on a large amount of information. At the same time, the amount of information on the Web is continuously growing, especially due to increased User Generated Content since the apparition of Web 2.0. In this paper, we propose a method that exploits blog textual data in order to supply a recommender system. The method we propose has two steps. First, subjective texts are labelled according to their expressed opinion in order to build a user-item-rating matrix. Second, this matrix is used to establish recommendations thanks to a collaborative filtering technique. Keywords-Opinion classification; User Generated Content; Recommender systems; Collaborative filterin