Multi-objective Automatic Algorithm Configuration for the Classification Problem of Imbalanced Data

Multi-objective Automatic Algorithm Configuration for the Classification Problem of Imbalanced Data

S. Tari, N. Szczepanski, L. Mousin, J. Jacques, M.-E. Kessaci, L. Jourdan, Multi-objective Automatic Algorithm Configuration for the Classification Problem of Imbalanced Data, in: 2020 IEEE Congress on Evolutionary Computation (CEC), 2020: pp. 1-8

 

Abstract:

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.


Top