基于强化学习的特征选择算法  

Feature Selection Algorithm Based on Reinforcement Learning

作　　者：朱振国[1] 赵凯旋 刘民康 ZHU Zhen-Guo;ZHAO Kai-Xuan;LIU Min-Kang(School of Information Science and Engineering,Chongqing Jiaotong University,Chongqing 400074,China)

机构地区：[1]重庆交通大学信息科学与工程学院,重庆400074

出　　处：《计算机系统应用》2018年第10期214-218,共5页Computer Systems & Applications

摘　　要：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.

关 键 词：强化学习 特征选择 Q学习 特征子集 数据挖掘 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP311.13[自动化与计算机技术—控制科学与工程]