Multi-view consistency-driven robust feature selection method

doi:10.11772/j.issn.1001-9081.2025060685

Journal of Computer Applications ›› 2026, Vol. 46 ›› Issue (6): 1844-1854.DOI: 10.11772/j.issn.1001-9081.2025060685

• Data science and technology • Previous Articles

Multi-view consistency-driven robust feature selection method

Xue XU¹, Hu FAN¹, Yandan WANG², Xue DING¹, Xuefeng GAO¹, Bo ZHANG¹, Bo LIU¹, Beihong JIN²()

^1.Information Center，China Tobacco Zhejiang Industrial Company Limited，Hangzhou Zhejiang 310008，China
^2.Institute of Software，Chinese Academy of Sciences，Beijing 100190，China

Received:2025-06-23 Revised:2025-09-23 Accepted:2025-09-29 Online:2025-10-15 Published:2026-06-10
Contact: Beihong JIN
About author:XU Xue， born in 1994， M. S.， engineer. Her research interests include data mining， machine learning.
FAN Hu， born in 1978， M. S.， engineer. His research interests include process data mining and analysis.
WANG Yandan， born in 1998， Ph. D. candidate. Her research interests include deep learning， intelligent data processing.
DING Xue， born in 1983， M. S.， senior engineer. Her research interests include process data analysis.
GAO Xuefeng， born in 1986， engineer. Her research interests include process data analysis.
ZHANG Bo， born in 1978， M. S.， senior engineer. His research interests include process data mining.
LIU Bo， born in 1981， engineer. His research interests include process data analysis.
First author contact:JIN Beihong， born in 1967， Ph. D.， professor. Her research interests include deep learning， distributed computing.
Supported by:
Science and Technology Program of China Tobacco Zhejiang Industrial Company Limited(ZJZY2024E003)

多视图一致性驱动的鲁棒特征选择方法

许雪¹, 樊虎¹, 王彦丹², 丁雪¹, 高雪峰¹, 张博¹, 刘博¹, 金蓓弘²()

^1.浙江中烟工业有限责任公司信息中心，杭州 310008
^2.中国科学院软件研究所，北京 100190

通讯作者: 金蓓弘
作者简介:许雪（1994—），女，山东菏泽人，工程师，硕士，主要研究方向：数据挖掘、机器学习
樊虎（1978—），男，湖北枝江人，工程师，硕士，主要研究方向：工艺数据挖掘与分析
王彦丹（1998—），女，山东青岛人，博士研究生，主要研究方向：深度学习、智能数据处理
丁雪（1983—），女，山东潍坊人，高级工程师，硕士，主要研究方向：工艺数据分析
高雪峰（1986—），女，河南社旗人，工程师，主要研究方向：工艺数据分析
张博（1978—），男，吉林洮南人，高级工程师，硕士，主要研究方向：工艺数据挖掘
刘博（1981—），男，湖北大悟人，工程师，主要研究方向：工艺数据分析
第一联系人：金蓓弘（1967—），女，浙江杭州人，教授，博士，主要研究方向：深度学习、分布式计算。
基金资助:
浙江中烟工业有限责任公司科技项目(ZJZY2024E003)

Abstract

Abstract:

Identifying important features from high-dimensional complex industrial data is crucial for production process anomaly monitoring. Aiming at the problem that the existing feature selection algorithms are difficult to model the complex intrinsic structure of data in the face of noise disturbance， a Multi-view Consistency-driven Robust feature selection method （MCR） was proposed. Firstly， a consistency-guided denoising mechanism with structure preservation was designed， in which multi-view collaborative modeling and inconsistency region detection were used to eliminate local noise disturbance while improving structural fidelity and integrity of the raw data. Then， a joint discriminative and consistency-driven feature fusion module was constructed， where high-quality multi-view embedding representations and a feature weight matrix were learned simultaneously， thereby enhancing the ability to perceive key feature dimensions. Finally， a cooperative sparse regularization-based feature selection strategy was introduced， so as to select the most discriminative and structurally consistent subset of features from the fused embedding space. Without relying on labeled information， this method achieves perception and selection of key feature dimensions through multi-view collaborative modeling and consistency-driven optimization. Extensive experimental results on several public benchmark datasets and a real-world cigarette production dataset demonstrate that MCR outperforms the existing mainstream feature selection methods such as Binary Horse herd Optimization Algorithm （BinHOA） and Improved Binary DJaya Algorithm （IBJA）， achieving classification accuracy improvements of 0.23 to 12.15 percentage points on public datasets and 2.22 to 5.00 percentage points on real industrial dataset， validating its robustness and effectiveness in complex scenarios.

Key words: feature selection, multi-view learning, unsupervised learning, noise robustness, multi-view consistency

摘要：

从高维复杂的工业数据中精准识别关键特征对于生产过程异常监测具有重要意义。针对现有特征选择算法面对噪声扰动难以建模数据复杂内在结构的问题，提出一种多视图一致性驱动的鲁棒特征选择方法（MCR）。首先，提出一种结构保持的一致性引导去噪机制，以通过多视图协同建模与不一致性区域检测，有效剔除局部噪声干扰，并提升原始数据的结构保真性与数据完整性；其次，构建联合判别与一致性驱动的特征融合模块，学习高质量的多视图嵌入表示与特征权重矩阵，从而提升对关键特征维度的感知能力；最后，引入一种基于协同稀疏正则化的特征选择机制，从融合后的嵌入空间中筛选出一个最具判别力和结构一致性的特征子集。该方法无需依赖标签信息，通过多视图协同建模与一致性驱动优化，实现对关键特征维度的感知与选择。在多个公开基准数据集以及一个真实的卷烟生产过程数据集上的大量实验结果表明，MCR在多个分类任务中相较于高效的二值马群优化算法（BinHOA）和二值化Jaya算法（IBJA）等现有的主流方法的分类准确率提升达0.23~12.15个百分点，在实际工业数据集上的分类准确率提升达2.22~5.00个百分点，验证了该方法在复杂场景下的鲁棒性与有效性。

关键词: 特征选择, 多视图学习, 无监督学习, 噪声鲁棒性, 多视图一致性

CLC Number:

TP181

Xue XU, Hu FAN, Yandan WANG, Xue DING, Xuefeng GAO, Bo ZHANG, Bo LIU, Beihong JIN. Multi-view consistency-driven robust feature selection method[J]. Journal of Computer Applications, 2026, 46(6): 1844-1854.

许雪, 樊虎, 王彦丹, 丁雪, 高雪峰, 张博, 刘博, 金蓓弘. 多视图一致性驱动的鲁棒特征选择方法[J]. 《计算机应用》唯一官方网站, 2026, 46(6): 1844-1854.

Figures/Tables 11

References 52

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数据集	样本数	特征维度	类别数
Image Segmentation	2 310	19	7
Heart Disease	270	13	2
Zoo	101	17	2
Online Shoppers	12 330	17	2
Parkinsons	757	754	3
Hepatitis	80	19	2
PenBased	10 992	16	10
Raisin	900	7	2

数据集	样本数	特征维度	类别数
Image Segmentation	2 310	19	7
Heart Disease	270	13	2
Zoo	101	17	2
Online Shoppers	12 330	17	2
Parkinsons	757	754	3
Hepatitis	80	19	2
PenBased	10 992	16	10
Raisin	900	7	2

方法	Image Segmentation		Heart Disease		Zoo		Online Shoppers		Parkinsons		Hepatitis		PenBased		Raisin
方法	SVM	NB	SVM	NB	SVM	NB	SVM	NB	SVM	NB	SVM	NB	SVM	NB	SVM	NB
全特征集	93.80	81.10	81.48	83.95	93.55	90.32	88.97	79.67	83.70	76.27	79.17	59.57	99.21	85.93	87.04	86.30
ANOVA-MV	73.88	87.97	54.77	52.56	75.68	96.77	87.84	79.72	86.68	87.37	83.42	55.46	97.67	82.87	83.77	86.03
MI-MV	73.73	87.88	55.47	59.78	73.76	97.39	86.50	83.27	85.88	78.35	81.52	77.20	96.82	83.79	82.58	86.01
RFE-MV	72.26	83.73	57.44	71.64	74.73	97.41	86.46	77.64	87.09	75.13	81.00	68.96	96.37	82.75	83.63	86.30
LASSO-MV	73.36	75.74	53.28	72.73	75.81	90.32	89.04	80.33	86.30	76.42	78.34	75.35	97.90	83.87	84.78	86.97
CFS	48.56		74.78		82.35
Consistency FS	49.72		64.35		82.65
FCBF	45.13		74.78		81.76
INTERACT	46.47		64.35		82.65
MRMI	80.55		75.11		77.94
SFE	75.76	66.81	71.60	69.14	80.65	87.10	89.43	84.64	84.58	78.41	83.33	75.00	89.96	69.44	85.56	84.44
ECML-MV	78.66	87.48	78.51	79.95	83.40	97.49	89.36	84.48	87.26	92.47	87.74	91.57	96.81	83.82	85.85	86.32
BinHOA			67.30				87.20			93.05	87.16
IBJA				88.70						91.40		91.30
MCR	85.19	88.90	90.19	92.09	95.55	98.80	89.66	85.62	89.14	94.58	89.34	92.43	98.75	85.63	88.67	87.56

方法	Image Segmentation		Heart Disease		Zoo		Online Shoppers		Parkinsons		Hepatitis		PenBased		Raisin
方法	SVM	NB	SVM	NB	SVM	NB	SVM	NB	SVM	NB	SVM	NB	SVM	NB	SVM	NB
全特征集	93.80	81.10	81.48	83.95	93.55	90.32	88.97	79.67	83.70	76.27	79.17	59.57	99.21	85.93	87.04	86.30
ANOVA-MV	73.88	87.97	54.77	52.56	75.68	96.77	87.84	79.72	86.68	87.37	83.42	55.46	97.67	82.87	83.77	86.03
MI-MV	73.73	87.88	55.47	59.78	73.76	97.39	86.50	83.27	85.88	78.35	81.52	77.20	96.82	83.79	82.58	86.01
RFE-MV	72.26	83.73	57.44	71.64	74.73	97.41	86.46	77.64	87.09	75.13	81.00	68.96	96.37	82.75	83.63	86.30
LASSO-MV	73.36	75.74	53.28	72.73	75.81	90.32	89.04	80.33	86.30	76.42	78.34	75.35	97.90	83.87	84.78	86.97
CFS	48.56		74.78		82.35
Consistency FS	49.72		64.35		82.65
FCBF	45.13		74.78		81.76
INTERACT	46.47		64.35		82.65
MRMI	80.55		75.11		77.94
SFE	75.76	66.81	71.60	69.14	80.65	87.10	89.43	84.64	84.58	78.41	83.33	75.00	89.96	69.44	85.56	84.44
ECML-MV	78.66	87.48	78.51	79.95	83.40	97.49	89.36	84.48	87.26	92.47	87.74	91.57	96.81	83.82	85.85	86.32
BinHOA			67.30				87.20			93.05	87.16
IBJA				88.70						91.40		91.30
MCR	85.19	88.90	90.19	92.09	95.55	98.80	89.66	85.62	89.14	94.58	89.34	92.43	98.75	85.63	88.67	87.56

方法	准确率	Micro-F1
全特征集	89.02	88.81
ANOVA-MV	86.63	86.46
MI-MV	86.04	86.83
RFE-MV	87.86	87.10
RF-MV	86.90	85.70
LASSO-MV	88.82	87.54
MCR	91.04	91.83

Multi-view consistency-driven robust feature selection method

多视图一致性驱动的鲁棒特征选择方法

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Figures/Tables 11

References 52

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Metrics

特征选择比例	方法	PenBased	Parkinsons	Cigarette Production
10	MI-MV	53.24	78.14	43.26
	RFE-MV	51.61	76.44	44.78
	ANOVA-MV	56.34	78.14	46.37
	LASSO-MV	53.85	73.05	47.32
	MCR	55.58	79.66	50.58
20	MI-MV	81.60	83.05	82.72
	RFE-MV	83.42	83.11	81.57
	ANOVA-MV	83.84	82.83	86.72
	LASSO-MV	83.78	83.57	86.24
	MCR	84.32	84.75	87.28

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方法	PenBased	Parkinsons	Cigarette Production
MCR	85.63	94.58	91.04
w/o噪声修正	83.09	93.15	87.39
w/o多视图学习	84.28	93.47	87.82
w/o线性视图	84.54	94.16	88.34
w/o流形视图	84.89	93.51	87.94
w/o聚类视图	84.64	94.03	88.29
w/o稀疏正则化	84.56	93.84	88.98
w/o自适应融合	84.39	93.66	89.10
w/o噪声修正+多视图学习	81.14	91.44	83.57
w/o多视图学习+稀疏正则化	82.61	92.13	85.16
w/o噪声修正+自适应融合	81.25	91.63	84.85

方法	PenBased	Parkinsons	Cigarette Production
MCR	85.63	94.58	91.04
w/o噪声修正	83.09	93.15	87.39
w/o多视图学习	84.28	93.47	87.82
w/o线性视图	84.54	94.16	88.34
w/o流形视图	84.89	93.51	87.94
w/o聚类视图	84.64	94.03	88.29
w/o稀疏正则化	84.56	93.84	88.98
w/o自适应融合	84.39	93.66	89.10
w/o噪声修正+多视图学习	81.14	91.44	83.57
w/o多视图学习+稀疏正则化	82.61	92.13	85.16
w/o噪声修正+自适应融合	81.25	91.63	84.85