Extraction de biclusters contraints dans des contextes bruités

National audienceL'extraction de biclusters, qui consiste à rechercher un groupe d'attributs qui montrent un comportement cohérent pour un sous-ensemble d'observations dans une matrice de données, est une tâche importante dans divers domaines, telle que la biologie. Nous proposons ici un nouveau système, COBIC, qui combine des algorithmes de graphes avec des méthodes de fouille de données pour une recherche efficace de biclusters pertinents et susceptibles de se recouvrir. COBIC est fondé sur les algorithmes de flot maximal/coupe minimale et est capable de prendre en compte les connaissances d'une base exprimées sous forme d'une classification, par un mécanisme d'adaptation des poids lors de l'extraction itérative des régions denses. L'évaluation de COBIC sur des données réelles et la comparaison par rapport à des méthodes efficaces de biclustering montrent que COBIC est très performant et en particulier lorsque la qualité des biclusters s'évalue en fonction de la significativité de l'enrichissement des clusters calculés avec les fonctions cellulaires décrites dans l'Ontologie GO

Tractable Induction and Classification in First Order Logic Via Stochastic Matching

Learning in first-order logic (FOL) languages suffers from a specific difficulty: both induction and classification are potentially exponential in the size of hypotheses. This difficulty is usually dealt with by limiting the size of hypotheses, via either syntactic restrictions or search strategies. This paper is concerned with polynomial induction and use of FOL hypotheses with no size restrictions. This is done via stochastic matching: instead of exhaustively exploring the set of matchings between any example and any short candidate hypothesis, one stochastically explores the set of matchings between any example and any candidate hypothesis. The user sets the number of matching samples to consider and thereby controls the cost of induction and classification. One advantage of this heuristic is to allow for resource-bounded learning, without any a priori knowledge about the problem domain. Experiments on a real-world problem pertaining to organic chemistry fully demonstrate the potent..

Constraint Inductive Logic Programming

. This paper is concerned with learning from positive and negative examples expressed in first-order logic with numerical constants. The presented approach is based on the cooperation of Inductive Logic Programming (ILP) and Constraint Logic Programming (CLP), and proceeds as follows: ffl A discriminant induction problem is shown to be equivalent to a Constraint Satisfaction Problem (CSP): all constrained clauses covering positive examples and rejecting negative examples can be trivially derived from the solutions of this CSP. ffl Solving this CSP then allows to build the G set of solutions in terms of Version Spaces; this resolution can be delegated to a constraint solver. ffl This CSP provides a tractable computational characterization of G, which is sufficient to classify further examples and offers simple countingbased heuristics to resist noisy data. In this hybrid ILP-CLP approach, CLP performs most of the search involved in inductive learning; the advantage is to benefit fro..