Reinforcement learning based distance metric filtering approach in clustering

Conventional feature selection methods may not provide sufficient means to deal with the diverse growing amount of data nowadays. Evolutionary Distance Metric Learning (EDML) relies on an evolutionary approach in its distance metric learning process; this process in case of diagonal EDML can be viewed as an embedded feature weighting one. However, such process is done simultaneously on all features and does not explicitly select the features. This paper introduces a new hybrid system R-EDML, combining the sequential decision making of Reinforcement Learning (RL) with the evolutionary feature prioritizing process of EDML in clustering. The goal is to create a feature selection control strategy that aims to optimize the input space by reducing the number of selected features while maintaining the clustering performance. This can lead to future data collection time and cost reduction. In the proposed method, features represented by the elements of EDML distance transformation matrices are prioritized by a differential evolution algorithm. Then a selection control strategy using reinforcement learning is learned by sequentially inserting and evaluating the prioritized elements. This process is repeated with the aim to optimize the matrices by filtering the elements used in them. The outcome is the selection of the best R-EDML generation matrices with the least number of elements possible. R-EDML was compared to normal EDML in terms of feature selection and accuracy. Results show a decrease in the number of features compared to EDML, while maintaining a similar accuracy level.