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计算机系统应用:2018,27(10):214-218
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基于强化学习的特征选择算法
朱振国, 赵凯旋, 刘民康
(重庆交通大学 信息科学与工程学院, 重庆 400074)
Feature Selection Algorithm Based on Reinforcement Learning
ZHU Zhen-Guo, ZHAO Kai-Xuan, LIU Min-Kang
(School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China)
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投稿时间:2018-03-13    修订日期:2018-03-20
中文摘要: 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.
Abstract: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.
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朱振国,赵凯旋,刘民康.基于强化学习的特征选择算法.计算机系统应用,2018,27(10):214-218
ZHU Zhen-Guo,ZHAO Kai-Xuan,LIU Min-Kang.Feature Selection Algorithm Based on Reinforcement Learning.COMPUTER SYSTEMS APPLICATIONS,2018,27(10):214-218

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