On the Influence of the instance structure for metaheuristic performances -- Application to a graph drawing problem

International audienceMetaheuristics are now so common that for some classical hard combinatorial problems, there exist more than ten variants. Thus, the issue of comparing optimization methods is crucial. In this paper, we focus on one aspect of this question: the impact of the choice of the test instances on the metaheuristic performances and the possible link with the fitness landscape structure. We base our experimental framework on the arc crossing minimization problem for layered digraphs. We compare a hybridized genetic algorithm and a multistart descent which are among the best approaches to this problem. We worked on two instance families with various sizes and structural complexities: small graphs which are easy to draw on a standard size support, and large graphs specifically built for our experiments. We show that, for the smallest instances, there is no significant difference between methods whereas for graphs similar to those classically used nowadays in applications the genetic algorithm is better, and for the largest graphs (with a scaling factor up to $10^{300}$), the multistart descent is the best method. These results suggest that for ``structured'' fitness landscapes associated with real-life instances the GA exploits its implicit learning. On the other hand for very large landscapes with probably numerous local optima, only one exploration on a larger scale can be provided by local searches from a random starting point, cheap in computing effort

Un guide sur la toile pour sélectionner un logiciel de tracé de graphes

Nouvelle adresse du site : http://gvsr.polytech.univ-nantes.frNational audienceLes graphes permettent d'une part, en tant qu'objets combinatoires, de modéliser et d'analyser des systèmes de relations complexes entre des entités et d'autre part, de représenter ces relations sur des supports visuels afin de les rendre accessibles à un utilisateur non spécialiste. Si ces derniers sont rapidement devenus des outils privilégiés pour de nombreuses problématiques applicatives comme la découverte de relations non explicites en veille technologique, le choix d'une méthode de visualisation efficace reste encore très souvent une question ouverte pour l'utilisateur. Afin de guider ce dernier dans sa sélection, nous dressons une typologie succincte des principaux modes de représentation à savoir le tracé statique, le tracé interactif et le tracé des très grands graphes, et nous finissons par la présentation d'un nouveau site Web, dédié au référencement des logiciels de tracés de graphes. Le site est consultable à l'adresse : http://hulk.knowesis.fr/GVSR http://gvsr.polytech.univ-nantes.fr. Son originalité réside en particulier dans une présentation homogène des informations pertinentes mises en œuvre au travers d'un ensemble de fiches codées en XML

Directed binary hierarchies and directed ultrametrics

Les hiérarchies binaires orientées ont été introduites pour fournir une représentation graphique orientée d'une famille de règles implicatives d'association. Une telle structure étend d'une façon très spécifique celle sous jacente aux arbres binaires hiérarchiques de classification. Nous proposons ici une formalisation précise de ce nouveau type de structure. Une hiérarchie binaire orientée est définie comme une famille de couples (ordonnés) de parties de l'ensemble à organiser remplissant des conditions spécifiques. Une nouvelle notion d'ultramétricité binaire orientée est construite. le résultat fondamental consiste en la mise en correspondance bijective entre une structure binaire ultramétrique orientée et une hiérarchie binaire orientée. De plus, un algorithme est proposé pour passer de la structure ultramétrique à celle graphique d'un arbre binaire orienté et valué

Directed binary hierarchies and directed ultrametrics

Directed binary hierarchies have been introduced in order to give a graphical reduced representation of a family of association rules. This type of structure extends in a very specific way that underlying binary hierarchical classification. In this paper an accurate formalization of this new structure is studied. A binary directed hierarchy is defined as a set of ordered pairs of subsets of the initial individual set satisfying specific conditions. New notion of directed ultrametricity is studied. The main result consists of establishing a bijective correspondence between a directed ultrametric space and a directed binary hierarchy. Moreover, an algorithm is proposed in order to transform a directed ultrametric structure into a graphical representation associated with a directed binary hierarchy

Semantics-based classification of rule interestingness measures

Assessing rules with interestingness measures is the cornerstone of successful applications of association rule discovery. However, as numerous measures may be found in the literature, choosing the measures to be applied for a given application is a difficult task. In this chapter, the authors present a novel and useful classification of interestingness measures according to three criteria: the subject, the scope, and the nature of the measure. These criteria seem essential to grasp the meaning of the measures, and therefore to help the user to choose the ones (s)he wants to apply. Moreover, the classification allows one to compare the rules to closely related concepts such as similarities, implications, and equivalences. Finally, the classification shows that some interesting combinations of the criteria are not satisfied by any index

VIPE: A NEW INTERACTIVE CLASSIFICATION FRAMEWORK FOR LARGE SETS OF SHORT TEXTS - APPLICATION TO OPINION MINING

International audienceThis paper presents a new interactive opinion mining tool that helps users to classify large sets of short texts originated from Web opinion polls, technical forums or Twitter. From a manual multi-label pre-classification of a very limited text subset, a learning algorithm predicts the labels of the remaining texts of the corpus and the texts most likely associated to a selected label. Using a fast matrix factorization, the algorithm is able to handle large corpora and is well-adapted to interactivity by integrating the corrections proposed by the users on the fly. Experimental results on classical datasets of various sizes and feedbacks of users from marketing services of the telecommunication company Orange confirm the quality of the obtained results

Découverte interactive de règles d'association via une interface visuelle

En nous appuyant sur des hypothèses majoritairement empruntées à des travaux sur les systèmes anthropocentrés d'aide à la décision, nous décrivons dans cet article un environnement interactif de fouille de règles d'association dans lequel l'utilisateur pilote le processus, en jouant le rôle d'une heuristique dans un environnement de recherche complexe. Afin de permettre à la fois une représentation visuelle accessible et une instanciation aisée des outils d'interactivité le modèle choisi est ici un graphe en niveaux - les niveaux étant associés aux cardinaux des sous-ensembles d'attributs des prémisses. Le processus a été déployé dans un logiciel prototype dont l'analyse des résultats ouvre de nouvelles perspectives sur l'analyse comportementale d'un utilisateur en situation de fouille

Analyse comparative de méthodologies et d'outils de construction automatique d'ontologies à partir de ressources textuelles

International audienceOver the recent years, several methodologies and tools for the automatic construction of ontologies from textual resources have been proposed. In this article we first analyze four of these approaches by comparing them with a reference approach - Methontology. We choose approaches which cover all the steps of the ontology construction process. Then, for some tools associated with the previously analyzed approaches, we analyze their performances and compare their results with manually obtained results

Selecting a multi-label classification method for an interactive system

International audienceInteractive classification-based systems engage users to coach learning algorithms to take into account their own individual preferences. However most of the recent interactive systems limit the users to a single-label classification, which may be not expressive enough in some organization tasks such as film classification, where a multi-label scheme is required. The objective of this paper is to compare the behaviors of 12 multi-label classification methods in an interactive framework where "good" predictions must be produced in a very short time from a very small set of multi-label training examples. Experimentations highlight important performance differences for 4 complementary evaluation measures (Log-Loss, Ranking-Loss, Learning and Prediction Times). The best results are obtained for Multi-label k Nearest Neighbours (ML-kNN), Ensemble of Classifier Chains (ECC) and Ensemble of Binary Relevance (EBR)