Dynamic feature selection algorithm based on Q-learning mechanism

Feature selection is a technique to improve the classification accuracy of classifiers and a convenient data visualization method. As an incremental, task oriented, and model-free learning algorithm, Q-learning is suitable for feature selection, this study proposes a dynamic feature selection algorithm, which combines feature selection and Q-learning into a framework. First, the Q-learning is used to construct the discriminant functions for each class of the data. Next, the feature ranking is achieved according to the all discrimination functions vectors for each class of the data comprehensively, and the feature ranking is doing during the process of updating discriminant function vectors. Finally, experiments are designed to compare the performance of the proposed algorithm with four feature selection algorithms, the experimental results on the benchmark data set verify the effectiveness of the proposed algorithm, the classification performance of the proposed algorithm is better than the other feature selection algorithms, meanwhile the proposed algorithm also has good performance in removing the redundant features, and the experiments of the effect of learning rates on the our algorithm demonstrate that the selection of parameters in our algorithm is very simple.