基于去噪熵的有限样本工业数据鲁棒特征选择

IF 9.9 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Chan Xu;Silu Chen;Xiangjie Kong;Chi Zhang;Guilin Yang;Zaojun Fang
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引用次数: 0

摘要

特征选择在高维和小样本数据中具有挑战性,特别是在具有不同噪声源的工业信息学中。特征噪声的信息熵包含在标签和被噪声破坏的特征的互信息中,可以去除这些信息以提高分类精度。在本文中,我们提出了一种鲁棒的特征选择方法,通过消除相关度量中的特征噪声。特征噪声建模为零均值截尾正态分布,基于最大熵原理求解方差方程确定其熵。然后,提出了一种特征传输的噪声通道,提取与类相关的噪声分量。此外,通过去除互信息中的噪声熵,提出了一种无噪声互信息度量。最后,提出了一种基于无噪声互信息的相关性最大化和冗余最小化的新准则。实验结果证实了我们的方法在不同工业部门数据集上的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust Feature Selection by Removing Noise Entropy Within Mutual Information for Limited-Sample Industrial Data
Feature selection is challenging in high-dimensional and small-sample data, particularly in industrial informatics with diverse noise sources. The information entropy of feature noise is included in mutual information of a label and noise-corrupted features, which can be removed to increase classification accuracy. In this article, we propose a robust feature selection method by eliminating feature noise in the relevance measure. Feature noise is modeled as a zero-mean censored normal distribution, so its entropy is determined by solving the variance equation based on the maximum entropy principle. Then, a noisy channel for feature transmission is proposed to extract class-relevant noise component. Furthermore, a noise-free mutual information metric is developed by removing noise entropy within mutual information. Eventually, a novel criterion is proposed by maximizing relevance based on noise-free mutual information while minimizing redundancy. Experimental results confirm the effectiveness of our approach on datasets from various industrial sectors.
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来源期刊
IEEE Transactions on Industrial Informatics
IEEE Transactions on Industrial Informatics 工程技术-工程:工业
CiteScore
24.10
自引率
8.90%
发文量
1202
审稿时长
5.1 months
期刊介绍: The IEEE Transactions on Industrial Informatics is a multidisciplinary journal dedicated to publishing technical papers that connect theory with practical applications of informatics in industrial settings. It focuses on the utilization of information in intelligent, distributed, and agile industrial automation and control systems. The scope includes topics such as knowledge-based and AI-enhanced automation, intelligent computer control systems, flexible and collaborative manufacturing, industrial informatics in software-defined vehicles and robotics, computer vision, industrial cyber-physical and industrial IoT systems, real-time and networked embedded systems, security in industrial processes, industrial communications, systems interoperability, and human-machine interaction.
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