Binary Archimedes Optimization Algorithm based Feature Selection for Regression Problem

The use of datasets became paramount in many searches in one hand, on the other hand the rapidly growth of data size involves computational complexity and reduces model performances, this encourage us to find new methods to deal with this problem. Features Selection is the one of the main task used to resolve this issue. In this paper we propose a novel features selection method for regression task based on AOA (Archimedes Optimization Algorithm), experimental results shows that the proposed method can efficiently reduce dataset size and improve model performance.