基于相对离群因子的标签噪声过滤方法

侯森寓; 姜高霞; 王文剑

doi:10.16383/j.aas.c230117

基于相对离群因子的标签噪声过滤方法

doi: 10.16383/j.aas.c230117

侯森寓^1,,
姜高霞^1,,
王文剑^{1, 2,}

1.
山西大学计算机与信息技术学院太原 030006
2.
计算智能与中文信息处理教育部重点实验室太原 030006

基金项目: 国家自然科学基金(U21A20513, 62276161, 62076154), 中央引导地方科技发展基金(YDZX20201400001224), 山西省1331工程重点学科建设基金资助

详细信息

作者简介:
侯森寓：山西大学计算机与信息技术学院硕士研究生. 主要研究方向为机器学习, 标签噪声. E-mail: housenyu@163.com

姜高霞：山西大学计算机与信息技术学院副教授. 主要研究方向为机器学习, 数据挖掘. E-mail: jianggaoxia@sxu.edu.cn

王文剑：山西大学计算机与信息技术学院教授. 主要研究方向为机器学习, 数据挖掘. 本文通信作者. E-mail: wjwang@sxu.edu.cn

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出版历程
- 收稿日期: 2023-03-08
- 网络出版日期: 2023-07-10
- 刊出日期: 2024-01-29

A Label Noise Filtering Method Based on Relative Outlier Factor

1.
School of Computer and Information Technology, Shanxi University, Taiyuan 030006
2.
Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Taiyuan 030006

Funds: Supported by National Natural Science Foundation of China (U21A20513, 62276161, 62076154), Central Government Guides Local Science and Technology Innovation Projects (YDZX20201400001224), and Fund for the Shanxi Province 1331 Project of Key Subjects Construction

More Information

Author Bio:
HOU Sen-Yu　Master student at the School of Computer and Information Technology, Shanxi Univer-sity. His research interest covers machine learning and label noise

JIANG Gao-Xia　Associate profe-ssor at the School of Computer and Information Technology, Shanxi University. His research interest covers machine learning and data mining

WANG Wen-Jian　Professor at the School of Computer and Information Technology, Shanxi Univer-sity. Her research interest covers machine learning and data mining. Corresponding author of this paper

摘要

摘要: 分类任务中含有类别型标签噪声是传统数据挖掘中的常见问题, 目前还缺少针对性方法来专门检测类别型标签噪声. 离群点检测技术能用于噪声的识别与过滤, 但由于离群点与类别型标签噪声并不具有一致性, 使得离群点检测算法无法精确检测分类数据集中的标签噪声. 针对这些问题, 提出一种基于离群点检测技术、适用于过滤类别型标签噪声的方法 —— 基于相对离群因子(Relative outlier factor, ROF)的集成过滤方法(Label noise ensemble filtering method based on relative outlier factor, EROF). 首先, 通过相对离群因子对样本进行噪声概率估计; 然后, 再迭代联合多种离群点检测算法, 实现集成过滤. 实验结果表明, 该方法在大多数含有标签噪声的数据集上, 都能保持优秀的噪声识别能力, 并显著提升各种分类模型的泛化能力.
- 分类 /
- 标签噪声 /
- 离群点检测 /
- 相对离群因子 /
- 噪声过滤
Abstract: The presence of categorical label noise in classification tasks is a common issue in traditional data mining. Currently, there is a lack of targeted methods specifically designed to detect categorical label noise. While outlier detection techniques can be used for noise identification and filtering, the lack of consistency between outliers and categorical label noise renders outlier detection algorithms unable to accurately detect label noise in classification data sets. To address these issues, a method based on outlier detection techniques, called the label noise ensemble filtering method based on relative outlier factor (ROF) (EROF), is proposed for filtering categorical label noise. The EROF method estimates noise probability of samples by using relative outlier factor and then iteratively combinings multiple outlier detection algorithms for ensemble filtering. Experimental results show that this method maintains excellent noise identification capability in most data sets which contain label noise, and significantly improves the generalization ability of various classification models.
- Classification /
- label noise /
- outlier detection /
- relative outlier factor (ROF) /
- noise filtering

HTML全文

图 1 不同含噪情况下的分类模型决策边界

Fig. 1 Decision boundary of classification model in different cases with noise

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图 2 样本A的同质、异质样本

Fig. 2 Homogeneous and heterogeneous samples of sample A

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图 3 噪声样本A与真实样本G的绝对离群因子

Fig. 3 Homogeneous and heterogeneous absolute outlier factor results of noise sample A and real sample G

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图 4 数据的相对离群因子

Fig. 4 Relative outlier factors for data

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图 5 EROF算法流程示意图

Fig. 5 Flowchart diagram of the EROF Algorithm

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图 6 Wine数据集上, 基检测器噪声过滤对比

Fig. 6 Comparison of base detector noise filtering on Wine

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图 7 过滤比率 r 对过滤效果的影响

Fig. 7 Influence of filtering ratio r on filtering effect

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图 8 各算法噪声识别性能指标比较

Fig. 8 Comparison for noise recognition performance indicators of each algorithm

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图 9 各算法噪声识别性能指标的最优次数的占比比例

Fig. 9 Optimal frequency ratio for noise recognition performance indicators of each algorithm

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图 10 各分类模型分类准确率的临界差异图

Fig. 10 Critical difference diagram of classification accuracy of each classification model

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图 11 时间开销对比

Fig. 11 Running time comparison

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图 12 不同算法过滤后的准确率

Fig. 12 Accuracy after filtering by different algorithms

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图 13 MNIST上, 最后10次迭代的平均测试精度

Fig. 13 Average accuracy over the last 10epochs on MNIST

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表 1 数据集信息

Table 1 Information of data sets

序号	数据集名称	样本数	特征数	类别数
1	Wine	178	13	3
2	Sonar	208	60	2
3	Seeds	210	7	3
4	Heart	270	13	2
5	Votes	435	16	2
6	Musk	476	166	2
7	Wdbc	569	30	2
8	Australian	690	14	2
9	Credit Approval	690	15	2
10	Vehicle	846	18	4
11	Fourclass	862	2	2
12	German	1000	24	2
13	Svmguide3	1243	21	6
14	Isolet	1559	617	26
15	Segment	2310	18	7
16	Splice	3175	60	2
17	Satimage	4435	36	6
18	Banana	5300	2	2
19	Mushrooms	8124	22	2
20	Letter	20000	16	26

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表 2 UCI上, 不同噪声比例下的分类准确率

Table 2 Classification accuracy with different noise ratios on UCI

NR	序号	NoF	MNN	MVF	RD	CRF	vRD	Adp_mCRF	EROF	NR	序号	NoF	MNN	MVF	RD	CRF	vRD	Adp_mCRF	EROF
10%	1	0.926	0.981	0.963	0.921	0.943	0.963	0.963	0.981	20%	1	0.926	0.981	0.981	0.895	0.888	0.926	0.907	1.000
	2	0.762	0.714	0.730	0.713	0.615	0.746	0.635	0.746		2	0.761	0.762	0.746	0.745	0.669	0.762	0.683	0.762
	3	0.905	0.921	0.937	0.852	0.883	0.889	0.905	0.905		3	0.778	0.937	0.921	0.753	0.852	0.778	0.873	0.937
	4	0.691	0.753	0.728	0.665	0.675	0.691	0.704	0.778		4	0.704	0.741	0.704	0.688	0.695	0.704	0.716	0.753
	5	0.901	0.885	0.901	0.852	0.878	0.885	0.901	0.908		5	0.878	0.794	0.802	0.851	0.836	0.878	0.855	0.924
	6	0.841	0.841	0.848	0.793	0.807	0.833	0.826	0.902		6	0.818	0.765	0.765	0.795	0.792	0.833	0.826	0.841
	7	0.727	0.671	0.713	0.687	0.667	0.720	0.699	0.727		7	0.678	0.622	0.650	0.659	0.693	0.685	0.713	0.715
	8	0.918	0.901	0.912	0.891	0.897	0.936	0.936	0.959		8	0.825	0.830	0.865	0.825	0.780	0.842	0.819	0.930
	9	0.826	0.855	0.831	0.777	0.785	0.816	0.807	0.860		9	0.758	0.787	0.768	0.746	0.720	0.763	0.749	0.855
	10	0.841	0.850	0.870	0.804	0.802	0.841	0.831	0.889		10	0.773	0.792	0.739	0.750	0.751	0.773	0.768	0.845
	11	0.654	0.610	0.646	0.630	0.578	0.650	0.591	0.606		11	0.598	0.563	0.583	0.588	0.536	0.610	0.547	0.587
	12	0.969	1.000	1.000	0.931	0.933	0.965	0.965	1.000		12	0.942	1.000	0.977	0.911	0.916	0.954	0.938	1.000
	13	0.697	0.683	0.687	0.681	0.661	0.697	0.683	0.753		13	0.663	0.650	0.663	0.630	0.570	0.663	0.600	0.720
	14	0.786	0.775	0.786	0.748	0.695	0.772	0.713	0.812		14	0.716	0.681	0.724	0.679	0.658	0.708	0.676	0.794
	15	0.720	0.726	0.733	0.689	0.676	0.716	0.705	0.733		15	0.662	0.736	0.733	0.641	0.627	0.660	0.652	0.737
	16	0.931	0.945	0.945	0.889	0.915	0.931	0.938	0.957		16	0.905	0.928	0.935	0.865	0.867	0.908	0.895	0.958
	17	0.873	0.870	0.879	0.839	0.849	0.872	0.867	0.893		17	0.817	0.823	0.840	0.801	0.781	0.818	0.818	0.897
	18	0.926	0.936	0.928	0.889	0.884	0.927	0.926	0.965		18	0.854	0.881	0.864	0.830	0.820	0.858	0.862	0.958
	19	0.972	0.984	0.978	0.942	0.931	0.970	0.969	1.000		19	0.887	0.920	0.930	0.860	0.839	0.882	0.880	0.998
	20	0.929	0.920	0.941	0.896	0.905	0.921	0.925	0.949		20	0.868	0.855	0.923	0.833	0.829	0.852	0.855	0.940
30%	1	0.833	0.944	0.907	0.743	0.686	0.759	0.704	0.963	40%	1	0.630	0.926	0.815	0.588	0.686	0.611	0.704	0.981
	2	0.603	0.571	0.587	0.580	0.603	0.603	0.619	0.635		2	0.540	0.508	0.508	0.507	0.585	0.524	0.603	0.508
	3	0.841	0.952	0.921	0.772	0.725	0.794	0.762	0.957		3	0.603	0.794	0.825	0.609	0.590	0.635	0.603	0.921
	4	0.407	0.420	0.395	0.394	0.580	0.407	0.605	0.618		4	0.457	0.481	0.457	0.458	0.423	0.469	0.444	0.580
	5	0.710	0.649	0.672	0.652	0.639	0.672	0.664	0.870		5	0.580	0.550	0.511	0.580	0.649	0.603	0.664	0.718
	6	0.750	0.765	0.795	0.720	0.692	0.750	0.720	0.818		6	0.629	0.674	0.667	0.617	0.591	0.644	0.606	0.727
	7	0.580	0.594	0.594	0.576	0.597	0.601	0.622	0.601		7	0.573	0.497	0.503	0.561	0.550	0.573	0.573	0.524
	8	0.684	0.737	0.731	0.666	0.621	0.684	0.649	0.860		8	0.632	0.655	0.661	0.621	0.639	0.643	0.667	0.766
	9	0.647	0.696	0.696	0.617	0.585	0.643	0.604	0.807		9	0.507	0.531	0.512	0.491	0.490	0.512	0.507	0.565
	10	0.676	0.647	0.657	0.656	0.649	0.676	0.667	0.773		10	0.551	0.556	0.546	0.547	0.565	0.575	0.589	0.700
	11	0.531	0.543	0.524	0.502	0.487	0.520	0.512	0.587		11	0.453	0.476	0.472	0.415	0.459	0.429	0.476	0.555
	12	0.811	0.961	0.927	0.774	0.794	0.815	0.826	0.973		12	0.683	0.776	0.757	0.669	0.631	0.699	0.656	0.764
	13	0.637	0.633	0.647	0.629	0.586	0.643	0.603	0.693		13	0.580	0.620	0.627	0.539	0.551	0.567	0.573	0.577
	14	0.651	0.601	0.627	0.627	0.632	0.641	0.649	0.713		14	0.633	0.576	0.582	0.608	0.598	0.622	0.622	0.641
	15	0.605	0.712	0.722	0.541	0.546	0.562	0.562	0.705		15	0.545	0.739	0.720	0.480	0.465	0.494	0.483	0.703
	16	0.834	0.922	0.896	0.782	0.777	0.815	0.805	0.948		16	0.688	0.885	0.874	0.677	0.646	0.694	0.680	0.929
	17	0.740	0.780	0.778	0.717	0.706	0.744	0.739	0.891		17	0.646	0.713	0.707	0.638	0.634	0.653	0.651	0.884
	18	0.736	0.764	0.747	0.727	0.721	0.744	0.749	0.924		18	0.603	0.618	0.615	0.571	0.593	0.600	0.613	0.701
	19	0.778	0.870	0.857	0.747	0.752	0.774	0.776	0.950		19	0.652	0.766	0.740	0.614	0.626	0.640	0.645	0.781
	20	0.791	0.855	0.896	0.747	0.764	0.782	0.784	0.930		20	0.675	0.852	0.857	0.640	0.642	0.666	0.671	0.922

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表 3 MNIST上的噪声识别性能

Table 3 Noise recognition performance on MNIST

评价指标	MNN	MVF	RD	CRF	vRD	Adp_mCRF	EROF
Acc	0.664	0.732	0.791	0.780	0.801	0.844	0.872
NfAcc	0.372	0.436	0.489	0.545	0.563	0.651	0.713
Re	0.582	0.669	0.739	0.761	0.763	0.816	0.851
Spec	0.993	0.985	0.998	0.858	0.951	0.956	0.957
Pre	0.997	0.995	0.999	0.964	0.987	0.988	0.991
F1	0.735	0.797	0.850	0.837	0.851	0.885	0.916

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