Reinforcement learning for evolutionary distance metric learning systems improvement

This paper introduces a hybrid system called R-EDML, combining the sequential decision making of Reinforcement Learning (RL) with the evolutionary feature prioritizing process of Evolutionary Distance Metric Learning (EDML) in clustering aiming to optimize the input space by reducing the number of selected features while maintaining the clustering performance. In the proposed method, features represented by the elements of EDML distance transformation matrices are prioritized. Then a selection control strategy using Reinforcement Learning is learned. R-EDML was compared to normal EDML and conventional feature selection. Results show a decrease in the number of features, while maintaining a similar accuracy level.