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<dc:title xml:lang="en">Metaheuristics and machine learning algorithms for the inventive design problem-solving loop</dc:title>
<dcterms:alternative xml:lang="fr">Métaheuristiques et algorithmes d'apprentissage automatique pour la boucle de résolution des problémes de conception inventive</dcterms:alternative>
<dc:subject xml:lang="fr">Méta-heuristique</dc:subject>
<dc:subject xml:lang="fr">OCAnt-Miner</dc:subject>
<dc:subject xml:lang="fr">Importance des fonctionnalités</dc:subject>
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<dc:subject xml:lang="fr">Classification</dc:subject>
<dc:subject xml:lang="fr">Identification automatique des contradictions</dc:subject>
<dc:subject xml:lang="fr">Données déséquilibrées</dc:subject>
<dc:subject xml:lang="fr">Ensembles de données synthétiques</dc:subject>
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<dc:subject xml:lang="en">Feature importance</dc:subject>
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<dc:subject xml:lang="en">Automatic contradiction identification</dc:subject>
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<dcterms:abstract xml:lang="fr">Ce travail s’inscrit dans la construction d’une démarche de conception inventive soutenue par l’analyse digitale des données techniques. Elle contribue à l’identification et à l’expression automatique des contradictions d’évolution d’un système technique sous forme de règles à partir de données décrivant la relation entre les paramètres de conception de ce système et les performances attendues. Les principales contributions sont une méthode d'identification des variables intervenant dans les contradictions, des mesures d'évaluation des règles impliquées dans les contradictions et un classifieur à base de règles appelé OCAnt-Miner d’obtention des règles à partir d’un tableau de données issues d’expériences ou de simulations. L’algorithme OCAnt-Miner a la propriété de bien traiter les données déséquilibrées, de réaliser une bonne discrimination entre les classes et d’exprimer les règles avec les variables nécessaires à la description de fonction de discrimination.</dcterms:abstract>
<dcterms:abstract xml:lang="en">This work is part of an inventive design approach supported by the digital analysis of technical data. It contributes to the automatic identification and expression of contradictions in the evolution of a technical system in the form of rules. These rules are based on data describing the relationship between the system's design parameters and its expected performance. The main contributions include a method for identifying the variables involved in contradictions, measures for evaluating the rules associated with contradictions, and a rule-based classifier called OCAnt-Miner for extracting rules from a dataset derived from experiments or simulations. The OCAnt-Miner algorithm excels at handling unbalanced data, achieving strong discrimination between classes, and expressing the rules with the necessary variables to describe the discrimination functions.</dcterms:abstract>
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