基于强化学习的特征选择算法

Feature Selection Algorithm Based on Reinforcement Learning

针对在数据挖掘过程中存在的维度灾难和特征冗余问题,本文在传统特征选择方法的基础上结合强化学习中Q学习方法,提出基于强化学习的特征选择算法,智能体Agent通过训练学习后自主决策得到特征子集.实验结果表明,本文提出的算法能有效的减少特征数量并有较高的分类性能.

For the dimensional disaster and feature redundancy problems in the process of data mining, a reinforcement learning based feature selection algorithm, which is combined Q learning methods with traditional feature selection methods, is proposed in this study. In the proposed method, the agent acquires a subset of characteristics autonomously through training and learning. Experimental results show that the proposed algorithm can effectively reduce the number of features and has higher classification performance.