摘要
由于信息技术的发展,各领域数据往往包含大量冗余特征,因此分类模型性能下降.特征选择是一种有效去除冗余特征的数据预处理技术,然而,已有的特征选择方法无法在选择有效特征的同时保证模型较高的分类准确率.为了解决该问题,提出了一种基于图卷积网络的强化学习特征选择方法.以深度Q网络为基本框架,将特征选择问题映射为马尔可夫决策过程.首先,设计了一种基于图卷积网络的状态表示方法,将特征子集转换成图结构,以高效地捕捉特征间关系;接着提出了一种综合考虑特征重要性、特征间相关性和分类性能的奖励函数引导智能体选择高Q值特征,以使得到的特征子集综合考虑几方面性能.在14个公共数据集和脑卒中筛查数据集上进行实验,结果表明:相比于已有的深度Q网络特征选择方法,所提方法在Congress数据集和脑卒中筛查数据集的准确率分别为99%和85%,分别提高了11.0%和3.6%,验证了该方法的有效性和可行性.
Due to the development of information technology,data in various fields often contain a large number of redundant features,which leads to the degradation of classification model performance.Feature selection is a data preprocessing technique that effectively removes redundant features.However,existing feature selection methods are unable to select effective features while ensuring high classification accuracy of the model.In order to solve this problem,a graph convolutional network-based reinforcement learning for feature selection is proposed.A deep Q-network is used as the basic framework to map the feature selection problem into a Markov decision process.Firstly,a state representation method based on graph convolutional network is designed to convert feature subsets into graph structures to capture inter-feature relationships efficiently.Then,a reward function considering feature importance,feature-to-feature correlation,and classification performance is designed to guide the agent to select high Q-value features,so that the obtained feature subset combines several aspects of performance.Experiments are performed on 14 public datasets and stroke screening datasets,and the results show that compared with the existing deep Q-network feature selection method,the accuracy of the proposed method on Congress dataset and the stroke screening dataset is 99%and 85%,respectively,which shows an improvement of 11%and 3.6%,respectively,and verifies the validity and feasibility of the method.
作者
谢静
李凤莲
阳佳
贾文辉
XIE Jing;LI Fenglian;YANG Jia;JIA Wenhui(College of Electronic Information Engineering,Taiyuan University of Technology,Taiyuan 030024,China;R&D Department,China Academy of Launch Vehicle Technology,Beijing 100076,China;Department of Neurology,Shanxi Provincial People’s Hospital,Taiyuan 030024,China)
出处
《湖南科技大学学报(自然科学版)》
北大核心
2025年第4期79-88,共10页
Journal of Hunan University of Science And Technology:Natural Science Edition
基金
国家自然科学基金项目资助(62171307)
山西省科技合作交流专项项目资助(202304041101035)。
关键词
特征选择
深度Q网络
马尔可夫决策过程
状态表示
奖励函数
feature selection
deep Q-network
Markov decision process
state representation
reward function
