Multi-objective Automatic Algorithm Configuration for the Classification Problem of Imbalanced Data
| Autor: | Marie-Eléonore Kessaci, Laetitia Jourdan, Nicolas Szczepanski, Lucien Mousin, Sara Tari, Julie Jacques |
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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), Institut Catholique de Lille (ICL), Université catholique de Lille (UCL), Projet Interreg Pathacov, European Project: 1.1.297 |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Algorithm configuration
021103 operations research Optimization problem business.industry Computer science 0211 other engineering and technologies 02 engineering and technology [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO] computer.software_genre Imbalanced data 0202 electrical engineering electronic engineering information engineering Task analysis 020201 artificial intelligence & image processing Local search (optimization) Data mining Sensitivity (control systems) business Protocol (object-oriented programming) computer ComputingMilieux_MISCELLANEOUS |
| Zdroj: | 2020 IEEE Congress on Evolutionary Computation (CEC) 2020 IEEE Congress on Evolutionary Computation (CEC), Jul 2020, Glasgow, United Kingdom. pp.1-8, ⟨10.1109/CEC48606.2020.9185785⟩ CEC |
| Popis: | Classification problems can be modeled as multi-objective optimization problems. MOCA-I is a multi-objective local search designed to solve these problems, particularly when the data are imbalanced. However, this algorithm has been tuned by hand in order to be efficient on particular datasets. In this paper, we propose a methodology to automatically conFigure a multi-objective algorithm for solving a supervised partial classification problem. This methodology is based on a multi-objective approach of automatic algorithm configuration and requires a clear definition of the experimental protocol. Therefore, we present a k-fold cross-validation protocol to train and test the configuration model. To the best of our knowledge, it is the first time that multi-objective automatic algorithm configuration is performed on optimization algorithms to solve classification problems. Experimental results on real imbalanced datasets show that our approach can find efficient configurations of MOCA-I with less effort in comparison with the ones found exhaustively by hand. |
| Databáze: | OpenAIRE |
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