非平衡概念漂移數據流主動(dòng)學(xué)習方法

李艷紅; 王甜甜; 王素格; 李德玉

doi:10.16383/j.aas.c230233

非平衡概念漂移數據流主動(dòng)學(xué)習方法

doi: 10.16383/j.aas.c230233

李艷紅^{1, 2,},
王甜甜^{1, 2,},
王素格^{1, 2,},
李德玉^{1, 2,}

1.
山西大學(xué)計算機與信息技術(shù)學(xué)院太原 030006
2.
山西大學(xué)計算智能與中文信息處理教育部重點(diǎn)實(shí)驗室太原 030006

基金項目: 國家重點(diǎn)研發(fā)項目(2022QY0300-01), 國家自然科學(xué)基金(62076158), 山西省基礎研究計劃項目(202203021221001)資助

詳細信息

作者簡(jiǎn)介:
李艷紅：山西大學(xué)計算機與信息技術(shù)學(xué)院副教授. 主要研究方向為數據挖掘, 機器學(xué)習. 本文通信作者. E-mail: liyh@sxu.edu.cn

王甜甜：山西大學(xué)計算機與信息技術(shù)學(xué)院碩士研究生. 主要研究方向為數據挖掘, 機器學(xué)習. E-mail: wttstu@163.com

王素格：山西大學(xué)計算機與信息技術(shù)學(xué)院教授. 主要研究方向為自然語(yǔ)言處理, 機器學(xué)習. E-mail: wsg@sxu.edu.cn

李德玉：山西大學(xué)計算機與信息技術(shù)學(xué)院教授. 主要研究方向為數據挖掘, 人工智能. E-mail: lidy@sxu.edu.cn

計量
- 文章訪(fǎng)問(wèn)數: 337
- HTML全文瀏覽量: 173
- PDF下載量: 121
- 被引次數: 0
出版歷程
- 收稿日期: 2023-04-24
- 錄用日期: 2023-10-12
- 網(wǎng)絡(luò )出版日期: 2024-01-25
- 刊出日期: 2024-03-29

Active Learning Method for Imbalanced Concept Drift Data Stream

LI Yan-Hong^{1, 2
,},
WANG Tian-Tian^{1, 2
,},
WANG Su-Ge^{1, 2
,},
LI De-Yu^{1, 2
,}

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, Shanxi University, Taiyuan 030006

Funds: Supported by National Key Research and Development Program of China (2022QY0300-01), National Natural Science Foundation of China (62076158), and Fundamental Research Program of Shanxi Province (202203021221001)

More Information

Author Bio:
LI Yan-Hong　Associate professor at the School of Computer and Information Technology, Shanxi University. Her research interest covers data mining and machine learning. Corresponding author of this paper

WANG Tian-Tian　Master student at the School of Computer and Information Technology, Shanxi University. Her research interest covers data mining and machine learning

WANG Su-Ge　Professor at the School of Computer and Information Technology, Shanxi University. Her research interest covers natural language processing and machine learning

LI De-Yu　Professor at the School of Computer and Information Technology, Shanxi University. His research interest covers data mining and artificial intelligence

摘要

摘要: 數據流分類(lèi)研究在開(kāi)放、動(dòng)態(tài)環(huán)境中如何提供更可靠的數據驅動(dòng)預測模型, 關(guān)鍵在于從實(shí)時(shí)到達且不斷變化的數據流中檢測并適應概念漂移. 目前, 為檢測概念漂移和更新分類(lèi)模型, 數據流分類(lèi)方法通常假設所有樣本的標簽都是已知的, 這一假設在真實(shí)場(chǎng)景下是不現實(shí)的. 此外, 真實(shí)數據流可能表現出較高且不斷變化的類(lèi)不平衡比率, 會(huì )進(jìn)一步增加數據流分類(lèi)任務(wù)的復雜性. 為此, 提出一種非平衡概念漂移數據流主動(dòng)學(xué)習方法(Active learning method for imbalanced concept drift data stream, ALM-ICDDS). 定義基于多預測概率的樣本預測確定性度量, 提出邊緣閾值矩陣的自適應調整方法, 使得標簽查詢(xún)策略適用于類(lèi)別數較多的非平衡數據流; 提出基于記憶強度的樣本替換策略, 將難區分、少數類(lèi)樣本和代表當前數據分布的樣本保存在記憶窗口中, 提升新基分類(lèi)器的分類(lèi)性能; 定義基于分類(lèi)精度的基分類(lèi)器重要性評價(jià)及更新方法, 實(shí)現漂移后的集成分類(lèi)器更新. 在7個(gè)合成數據流和3個(gè)真實(shí)數據流上的對比實(shí)驗表明, 提出的非平衡概念漂移數據流主動(dòng)學(xué)習方法的分類(lèi)性能優(yōu)于6種概念漂移數據流學(xué)習方法.
- 數據流分類(lèi) /
- 主動(dòng)學(xué)習 /
- 概念漂移 /
- 多類(lèi)不平衡
Abstract: Data stream classification researchs how to provide more reliable data-driven prediction models in open and dynamic environment. The key is how to detect and adapt to concept drift from continuously changing data stream that arrive in real-time. Currently, in order to detect concept drift and update classification models, data stream classification methods usually assume that the labels of all samples are known, which is unrealistic in real scenarios. Additionally, real data stream may exhibit a high and constantly changing class imbalance ratios, further increasing the complexity of the data stream classification task. In this paper, we propose an active learning method for imbalanced concept drift data stream (ALM-ICDDS). Firstly, we define a sample prediction certainty measure based on multiple prediction probabilities and propose an adaptive adjustment method for the margin threshold matrix, which makes the label query strategy suitable for imbalanced data stream with a number of categories. Then, we propose a sample replacement strategy based on memory strength, which saves the samples that are difficult-to-distinguish, minority class and represent the current data distribution in the memory window, and improves the classification performance of new base classifier. Finally, we define the importance evaluation and update method of base classifier based on classification accuracy, which realizes the ensemble classifier update after drift. Comparative experiments on seven synthetic data streams and three real data streams show that the active learning method for imbalance concept drift data stream is better than six concept drift data stream learning methods in classification performance.
- Data stream classification /
- active learning /
- concept drift /
- multi-class imbalance

HTML全文

圖 1 算法框架

Fig. 1 Algorithm framework

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圖 2 7種算法的ROC曲線(xiàn)

Fig. 2 ROC curves of seven algorithms

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圖 3 7種算法的精確率曲線(xiàn)

Fig. 3 Precision rate curves of seven algorithms

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圖 4 DS₆上消融實(shí)驗的結果

Fig. 4 Results of the ablation experiment on DS₆

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圖 5 參數$\beta $對算法的影響

Fig. 5 Effect of the parameter $\beta $ on the algorithm

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圖 9 參數$ n_d $對算法的影響

Fig. 9 Effect of the parameter $ n_d $ on the algorithm

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圖 6 參數$ \theta_{0} $對算法的影響

Fig. 6 Effect of the parameter $ \theta_{0} $ on the algorithm

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圖 7 參數n對算法的影響

Fig. 7 Effect of the parameter n on the algorithm

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圖 8 參數$ \alpha$對算法的影響

Fig. 8 Effect of the parameter $\alpha $ on the algorithm

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圖 10 不同類(lèi)型概念漂移數據流上的精確率曲線(xiàn)

Fig. 10 P curves on different types of concept drift data stream

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表 1 數據流特征

Table 1 Data stream feature

編號	數據流	樣本數	特征數	類(lèi)別數	類(lèi)分布	異常點(diǎn)(%)	漂移次數
1	DS₁	400000	25	15	類(lèi)平衡	0	0
2	DS₂	400000	25	15	類(lèi)平衡	5	3
3	DS₃	400000	25	15	(1/1/1/1/1/1/1/1/1/1/2/2/3/3/5)	0	0
4	DS₄	400000	25	15	(1/1/1/1/1/1/1/1/1/1/2/2/3/3/5)	5	3
5	DS₅	400000	25	15	(1/1/1/1/1/1/1/1/1/1/2/2/3/3/5),	0	0
5	DS₅	400000	25	15	(2/2/3/3/5/1/1/1/1/1/1/1/1/1/1)	0	0
6	DS₆	400000	25	15	(1/1/1/1/1/1/1/1/1/1/2/2/3/3/5),	5	3
6	DS₆	400000	25	15	(2/2/3/3/5/1/1/1/1/1/1/1/1/1/1)	5	3
7	DS₇	400000	25	50	類(lèi)平衡	5	3
8	Kddcup99_10%	494000	42	23	—	—	—
9	Shuttle	570000	10	7	—	—	—
10	PokerHand	830000	10	10	—	—	—

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表 2 概念漂移數據流特征

Table 2 Concept drift data stream feature

編號	數據流	概念漂移類(lèi)型	樣本數	特征數	類(lèi)別數	漂移寬度
1	DS₈	突變型	400000	25	15	1
2	DS₉	重復型	400000	25	15	1
3	DS₁₀	增量型	400000	25	15	10000
4	DS₁₁	逐漸型	400000	25	15	10000

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表 3 7種算法的P值(%)

Table 3 P value of seven algorithms (%)

數據流	LB	BOLE	ARFRE	CALMID	OALM-IDS	ALM-ICDDS-E	ALM-ICDDS
DS₁	96.89±0.31	96.36±0.11	98.07±0.43	98.01±0.41	98.03±0.25	97.18±0.48	99.07±0.34
DS₂	90.61±0.21	88.63±0.54	92.77±0.42	93.31±0.14	93.27±0.49	91.97±0.26	94.64±0.15
DS₃	94.41±0.11	96.07±0.23	96.74±0.45	96.64±0.34	96.75±0.56	96.46±0.61	97.84±0.24
DS₄	86.91±0.45	85.23±0.52	88.30±0.29	89.90±0.28	90.27±0.42	89.70±0.72	92.06±0.28
DS₅	93.60±0.48	94.04±0.52	96.30±0.18	94.65±0.49	95.47±0.32	94.24±0.35	96.17±0.19
DS₆	86.59±0.19	84.69±0.48	88.02±0.47	88.44±0.19	88.65±0.25	87.41±0.40	90.86±0.37
DS₇	88.25±0.86	87.21±0.79	90.16±0.92	90.49±0.47	90.51±0.53	89.32±0.38	93.67±0.40
Kddcup99_10%	83.85±0.59	81.10±0.15	85.56±0.54	92.12±0.45	92.13±0.31	91.24±0.51	95.80±0.17
Shuttle	64.63±0.42	63.85±0.27	79.07±0.31	85.35±0.14	85.70±0.32	83.48±0.25	85.99±0.13
PokerHand	51.63±0.39	50.36±0.35	52.51±0.56	53.93±0.28	54.57±0.50	52.90±0.18	55.89±0.51

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表 6 7種算法的${\rm{Kappa }}$值(%)

Table 6 ${\rm{Kappa }}$ value of seven algorithms (%)

數據流	LB	BOLE	ARFRE	CALMID	OALM-IDS	ALM-ICDDS-E	ALM-ICDDS
DS₁	95.09±0.43	95.47±0.26	97.11±0.33	97.84±0.18	97.52±0.50	96.31±0.53	98.72±0.18
DS₂	89.66±0.50	88.28±0.45	91.80±0.17	92.55±0.25	92.65±0.28	91.27±0.29	93.56±0.46
DS₃	93.08±0.13	95.68±0.22	95.62±0.53	96.50±0.46	96.46±0.60	96.05±0.36	97.69±0.21
DS₄	86.97±0.46	85.86±0.13	88.18±0.25	89.94±0.24	89.99±0.36	88.61±0.46	90.19±0.57
DS₅	92.32±0.37	94.18±0.45	95.86±0.28	94.40±0.50	95.52±0.14	94.29±0.20	95.81±0.35
DS₆	86.59±0.32	85.25±0.29	87.81±0.54	88.90±0.51	89.00±0.13	87.68±0.47	89.80±0.25
DS₇	88.28±0.46	87.51±0.97	89.93±0.71	90.01±0.92	90.19±0.40	89.51±0.59	93.67±0.54
Kddcup99_10%	80.94±0.22	75.68±0.25	79.36±0.35	83.32±0.24	85.83±0.50	84.87±0.16	86.81±0.33
Shuttle	58.73±0.39	61.54±0.22	73.78±0.20	79.39±0.43	80.11±0.53	80.97±0.24	83.56±0.54
PokerHand	50.34±0.58	49.86±0.40	50.36±0.16	51.24±0.21	51.39±0.16	50.55±0.41	52.25±0.35

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表 4 7種算法的R值(%)

Table 4 R value of seven algorithms (%)

數據流	LB	BOLE	ARFRE	CALMID	OALM-IDS	ALM-ICDDS-E	ALM-ICDDS
DS₁	94.78±0.13	96.04±0.24	96.81±0.59	97.87±0.24	97.92±0.25	96.15±0.31	98.63±0.17
DS₂	88.65±0.25	87.86±0.53	90.35±0.30	91.54±0.54	91.84±0.58	90.78±0.70	92.30±0.24
DS₃	92.55±0.45	95.92±0.32	94.80±0.43	96.12±0.14	97.92±0.54	95.99±0.52	98.55±0.29
DS₄	87.03±0.49	87.08±0.39	88.23±0.31	90.50±0.30	91.07±0.52	90.13±0.43	91.15±0.11
DS₅	91.54±0.11	92.33±0.51	96.04±0.20	93.82±0.55	94.94±0.27	92.91±0.42	96.53±0.42
DS₆	86.56±0.50	85.48±0.24	87.83±0.49	89.43±0.18	88.85±0.36	88.39±0.34	90.63±0.21
DS₇	87.19±0.42	86.12±0.11	87.29±0.36	88.41±0.50	88.77±0.43	87.87±0.20	91.61±0.78
Kddcup99_10%	60.89±0.50	63.05±0.50	58.26±0.38	61.88±0.38	63.71±0.54	63.42±0.67	69.34±0.57
Shuttle	61.40±0.21	50.84±0.31	54.36±0.35	59.52±0.41	63.12±0.59	61.79±0.16	64.59±0.29
PokerHand	43.57±0.30	44.78±0.46	55.21±0.60	56.84±0.11	52.77±0.54	55.36±0.25	59.57±0.43

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表 5 7種算法的${\rm{F}}1$值 (%)

Table 5 ${\rm{F}}1$ value of seven algorithms (%)

數據流	LB	BOLE	ARFRE	CALMID	OALM-IDS	ALM-ICDDS-E	ALM-ICDDS
DS₁	95.82±0.18	96.20±0.16	97.44±0.50	97.94±0.30	97.97±0.25	96.66±0.37	98.85±0.23
DS₂	89.62±0.23	88.24±0.53	91.54±0.35	92.42±0.22	92.55±0.53	91.37±0.43	93.46±0.18
DS₃	93.47±0.18	95.99±0.27	95.76±0.44	96.38±0.20	97.33±0.55	96.22±0.57	98.19±0.26
DS₄	86.97±0.47	86.15±0.45	88.26±0.30	90.20±0.29	90.67±0.46	89.91±0.59	91.60±0.16
DS₅	92.55±0.17	93.18±0.30	96.17±0.19	94.23±0.52	95.20±0.29	93.57±0.38	96.35±0.26
DS₆	86.57±0.27	85.08±0.32	87.92±0.48	88.93±0.18	88.75±0.30	87.90±0.35	90.74±0.27
DS₇	87.72±0.56	86.66±0.19	88.70±0.52	89.44±0.48	89.61±0.47	88.59±0.29	92.63±0.40
Kddcup99_10%	70.55±0.54	70.94±0.23	69.32±0.45	74.03±0.22	75.33±0.39	74.82±0.54	80.45±0.49
Shuttle	62.97±0.28	56.61±0.29	64.43±0.33	70.13±0.21	72.70±0.41	71.01±0.20	73.77±0.18
PokerHand	47.26±0.34	47.41±0.40	53.83±0.57	55.35±0.16	56.12±0.52	54.10±0.23	57.67±0.72

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