基于McDiarmid边界的自适应加权概念漂移检测方法

胡阳; 孙自强

doi:10.14135/j.cnki.1006-3080.20211215002

基于McDiarmid边界的自适应加权概念漂移检测方法

doi: 10.14135/j.cnki.1006-3080.20211215002

胡阳,
孙自强^,

华东理工大学能源化工过程智能制造教育部重点实验室, 上海200237

详细信息

作者简介:
胡阳（1996—），男，江西吉安人，硕士生，主要研究方向：数据流挖掘、概念漂移检测。E-mail：775115091@qq.com

通讯作者:
孙自强， E-mail：sunziqiang@ecust.edu.cn

中图分类号: TP391.4
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- 文章访问数: 11
- HTML全文浏览量: 13
- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2021-12-15
- 网络出版日期: 2022-04-12

Weight adaptive concept drift detection method based on McDiarmid boundary

HU Yang,
SUN Ziqiang^,

East China University of Science and Technology, Key Laboratory of Smart Manufactoring in Energy Chemical Process, Ministry of Education, Shanghai 200237

摘要

摘要: 针对概念漂移主动检测方法检测延迟高，易出现漏检、误报的问题，提出了一种基于McDiarmid边界的自适应加权概念漂移检测方法。引入衰减函数对分类结果加权，赋予旧数据更低权值，提升新数据的影响力。利用McDiarmid不等式得到加权分类正确率的置信边界，在检测到分类正确率下降超过置信边界时调节衰减因子，实现权值的动态改变。实验主要与DDM、RDDM、HDDM、FHDDM和ADWIN算法对比，结果表明，该算法具有最低的误报率和漏检率，且平均检测延迟和正确率在6种算法中排前2。
- 概念漂移 /
- 主动检测方法 /
- 数据流分类 /
- McDiarmid边界 /
- 衰减算法
Abstract: An adaptive weighted concept drift detection method based on McDiarmid boundary (WMDDM) is proposed to solve the problems of high detection delay, missed detection and false alarm in the active detection method of concept drift. WMDDM algorithm has a weight adjustment mechanism. The adaptive attenuation algorithm is introduced as a weight function to give the old data lower weights and dynamically adjust according to the changes in the data stream in order to adapt to the concept drift faster. The warning level and drift level of the weighted classification accuracy are obtained by McDiarmid's inequality. When it is detected that the weighted classification accuracy rate drops outside the drift level, the detection result is fed back to the classifier. When it is detected that the weighted classification accuracy rate drops beyond the warning level, the detector adapts to the change of the data flow through the triggered weight adjustment mechanism. The experiment uses 4 artificial data sets (two mutation drift data sets, two gradual drift data sets) and 1 real data set, which are mainly compared with Fast Hoeffding Drift Detection Method (FHDDM), Drift Detection Method based on the Hoeffding’s inequality (HDDM) and other algorithms. Experimental results show that the WMDDM algorithm has the lowest false alarm rate and missed detection rate, and the average detection delay and accuracy rate rank the top 2 among the six algorithms. Finally, WMDDM algorithm is used to classify real data sets and compared with FHDDM algorithm. The results show that WMDDM algorithm has a higher classification accuracy rate than FHDDM. Therefore, the WMDDM algorithm is suitable for abrupt and gradual conceptual drift, and has strong robustness.
- concept drift /
- active detection method /
- data stream classification /
- MacDiarmid’s boundary /
- attenuation algorithm

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图 1 实漂移和虚漂移概念图

Figure 1. Conceptual diagram of real drift and virtual drift

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图 2 概念漂移类型图

Figure 2. Concept drift type diagram

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图 3 加权窗口描述图

Figure 3. Weighted window description diagram

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图 4 WMDDM算法流程图

Figure 4. WMDDM algorithm flow chart

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图 5 检测点示意图

Figure 5. Schematic diagram of detection points

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图 6 在Electricity数据集上的分类准确率对比图

Figure 6. Comparison chart of classification accuracy on the Electricity dataset

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表 1 数据集特征表

Table 1. Data set feature table

Data set	Instances	Attributes	Category	Number Of srifts	Noise rate	Concept length	Drift type
SINE	100 000	2	2	4	10%	20 000	Sudden
MIXED	100 000	4	2	4	10%	20 000	Sudden
LED	100 000	24	10	3	10%	25 000	Gradual
CIRCLE	100 000	2	2	3	10%	25 000	Gradual
Electricity	45 312	7	2	Unknown	Unknown	Unknown	Unknown

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表 2 实验1结果

Table 2. Results of experiment 1

Detector	FPR/%	FNR/%	ADOD	Correct rate/%
WMDDM	0	0	37.25	85.34
WMDDM#1	80	0	44	85.32
FHDDM	0	0	44.00	85.32

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表 3 实验2结果

Table 3. Results of experiment 2

Detector	FPR/%	FNR/%	ADOD	Correct rate/%
WMDDM	0	0	69.67	86.25
WMDDM#1	81.25	0	49	86.26
FHDDM	0	0	74	86.24

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表 4 在SINE数据集上的实验结果

Table 4. Experimental results on the SINE dataset

Classifier	Detector	TP	FP	FN	FPR/%	FNR/%	ADOD	Correct rate/%
HT	WMDDM	4	1	0	20.0	0	47.00	86.36
	FHDDM	4	1	0	20.0	0	49.75	85.37
	HDDM	4	1	0	20.0	0	34.50	86.39
	DDM	4	0	0	0	0	150.50	86.05
	RDDM	4	3	0	42.9	0	100.25	86.08
	ADWIN	4	27	0	87.1	0	64.25	84.09
NB	WMDDM	4	0	0	0	0	37.25	85.34
	FHDDM	4	0	0	0	0	44.00	85.32
	HDDM	4	0	0	0	0	33.25	85.36
	DDM	4	0	0	0	0	152.75	85.06
	RDDM	4	0	0	0	0	101.00	85.17
	ADWIN	4	6	0	60.0	0	67.00	84.72

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表 5 在MIXED数据集上的实验结果

Table 5. Experimental results on the MIXED dataset

Classifier	Detector	TP	FP	FN	FPR/%	FNR/%	ADOD	Correct rate/%
HT	WMDDM	4	2	0	33.3	0	41.00	85.98
	FHDDM	4	7	0	63.6	0	41.00	85.24
	HDDM	4	5	8	59.3	0	35.75	85.38
	DDM	4	11	0	73.3	0	130.75	84.27
	RDDM	4	9	0	69.2	0	88.25	85.91
	ADWIN	4	9	0	69.2	0	76.00	84.82
NB	WMDDM	4	0	0	0	0	43.50	86.62
	FHDDM	4	0	0	0	0	45.50	86.62
	HDDM	4	0	0	0	0	33.75	86.60
	DDM	4	0	0	0	0	149.00	86.26
	RDDM	4	0	0	0	0	103.00	86.41
	ADWIN	4	6	0	60.0	0	67.50	86.03

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表 6 在LED数据集上的实验结果

Table 6. Experimental results on the LED dataset

Classifier	Detector	TP	FP	FN	FPR/%	FNR/%	ADOD	Correct rate/%
HT	WMDDM	3	0	0	0	0	237.67	89.68
	FHDDM	3	0	0	0	0	256.00	89.64
	HDDM	2	1	0	33.3	0	367.67	89.61
	DDM	0	3	3	100.0	100.0	400.0	89.53
	RDDM	2	2	1	50.0	33.3	380.67	89.59
	ADWIN	0	266	3	100.0	100.0	400.0	87.50
NB	WMDDM	3	0	0	0	0	237,67	89.68
	FHDDM	3	0	0	0	0	256.00	89.67
	HDDM	2	1	1	33.3	33.3	367.67	89.61
	DDM	0	3	3	100.0	100.0	400.0	89.53
	RDDM	2	2	1	50.0	33.3	380.67	89.59
	ADWIN	0	266	3	100.0	100.0	400.0	87.50

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表 7 在CIRCLE数据集上的实验结果

Table 7. Experimental results on the CIRCLE dataset

Classifier	Detector	TP	FP	FN	FPR/%	FNR/%	ADOD	Correct rate/%
HT	WMDDM	3	0	0	0	0	58.33	87.18
	FHDDM	3	0	0	0	0	61.33	87.16
	HDDM	3	0	0	0	0	44.33	87.19
	DDM	2	1	1	33.3	33.3	332.67	86.97
	RDDM	3	1	0	25.0	0	246.33	87.03
	ADWIN	3	4	0	57.1	0	93.67	86.74
NB	WMDDM	3	0	0	0	0	69.67	86.25
	FHDDM	3	0	0	0	0	74.00	86.24
	HDDM	3	0	0	0	0	41.33	86.26
	DDM	2	1	1	33.3	33.3	325.33	86.06
	RDDM	3	0	0	0	0	225.00	86.17
	ADWIN	3	2	0	40.0	0	113.67	86.20

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