A novel self-adaptive differential evolution for feature selection using threshold mechanism

Nowadays, most of databases for classification or regression consists of numerous features that describe the domain of interest. Therefore, they may have a huge influence on the results of classification/regression. A lot of research has shown that some features can be eliminated before the classification/ regression in order to obtain better results. In this paper, we propose a novel solution that is based on self-adaptive differential evolution for feature selection on a econometric database. A new solution is systematically presented in this paper. Results of the proposed feature selection method, according to the ROC-AUC score, overcome results, obtained without using it.

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