A hybrid CP/MOLS approach for multi-objective imbalanced classification
| Autor: | Christophe Lecoutre, Laetitia Jourdan, Nicolas Szczepanski, Gilles Audemard, Nadarajen Veerapen, Lucien Mousin |
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| Přispěvatelé: | Operational Research, Knowledge And Data (ORKAD), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Informatique de Lens (CRIL), Université d'Artois (UA)-Centre National de la Recherche Scientifique (CNRS), Faculté de gestion, économie et sciences [UCL, Lille] (FGES), Université catholique de Lille (UCL), Université Catholique de Lille - Faculté de gestion, économie et sciences (FGES), Institut Catholique de Lille (ICL), Université catholique de Lille (UCL)-Université catholique de Lille (UCL) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
business.industry
Computer science Pareto principle 0102 computer and information sciences 02 engineering and technology [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO] Machine learning computer.software_genre 01 natural sciences Class (biology) Multi-objective optimization Domain (software engineering) [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 010201 computation theory & mathematics Simple (abstract algebra) 0202 electrical engineering electronic engineering information engineering Constraint programming 020201 artificial intelligence & image processing Local search (optimization) Artificial intelligence business computer Partial classification ComputingMilieux_MISCELLANEOUS |
| Zdroj: | GECCO '21: Genetic and Evolutionary Computation Conference GECCO '21: Genetic and Evolutionary Computation Conference, Jul 2021, Lille France, France. pp.723-731, ⟨10.1145/3449639.3459310⟩ GECCO |
| DOI: | 10.1145/3449639.3459310⟩ |
| Popis: | In the domain of partial classification, recent studies about multiobjective local search (MOLS) have led to new algorithms offering high performance, particularly when the data are imbalanced. In the presence of such data, the class distribution is highly skewed and the user is often interested in the least frequent class. Making further improvements certainly requires exploiting complementary solving techniques (notably, for the rule mining problem). As Constraint Programming (CP) has been shown to be effective on various combinatorial problems, it is one such promising complementary approach. In this paper, we propose a new hybrid combination, based on MOLS and CP that are quite orthogonal. Indeed, CP is a complete approach based on powerful filtering techniques whereas MOLS is an incomplete approach based on Pareto dominance. Experimental results on real imbalanced datasets show that our hybrid approach is statistically more efficient than a simple MOLS algorithm on both training and tests instances, in particular, on partial classification problems containing many attributes. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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