Automatic Configuration of a Multi-objective Local Search for Imbalanced Classification

Automatic Configuration of a Multi-objective Local Search for Imbalanced Classification

S. Tari, H. Hoos, J. Jacques, M.-E. Kessaci, L. Jourdan, Automatic Configuration of a Multi-objective Local Search for Imbalanced Classification, in: T. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. Emmerich, H. Trautmann (Eds.), Parallel Problem Solving from Nature – PPSN XVI, Springer International Publishing, Cham, 2020: pp. 65-77.

 

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.

 

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