Abstract
MOCA-I is a multi-objective local search algorithm, based on the Pittsburgh representation, that has been formerly designed to solve partial classification problems with imbalanced data. Recently, multi-objective automatic algorithm configuration (MO-AAC) has proven effective in boosting the performance of multi-objective local search algorithms for combinatorial optimization problems. Here, for the first time, we apply MO-ACC to multi-objective local search for rule-based classification problems. Specifically, we present the Automatic Configuration of MOCA-I (AC-MOCA-I). AC-MOCA-I uses a methodology based on k-fold cross-validation to automatically configure an extended and improved version of MOCA-I. In a series of experiments on well-known datasets from the literature, we consider 183 456 unique configurations for MOCA-I and demonstrate that AC-MOCA-I leads to substantial improvements in performance. Moreover, we investigate the impact of the running time allotted to AC-MOCA-I on performance and the role of specific parameters and components.
Funded by the Pathacov Project of the Interreg France-Wallonie-Vlaanderen program, with the support of the European Regional Development Fund.
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Tari, S., Hoos, H., Jacques, J., Kessaci, ME., Jourdan, L. (2020). Automatic Configuration of a Multi-objective Local Search for Imbalanced Classification. In: Bäck, T., et al. Parallel Problem Solving from Nature – PPSN XVI. PPSN 2020. Lecture Notes in Computer Science(), vol 12269. Springer, Cham. https://doi.org/10.1007/978-3-030-58112-1_5
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