Journal of Shandong University(Engineering Science) ›› 2018, Vol. 48 ›› Issue (6): 44-55.doi: 10.6040/j.issn.1672-3961.0.2018.198

• Machine Learning & Data Mining • Previous Articles     Next Articles

An adaptive ensemble classification method based on deep attribute weighting for data stream

Yao LI(),Zhihai WANG*(),Yan′ge SUN,Wei ZHANG   

  1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
  • Received:2018-05-25 Online:2018-12-20 Published:2018-12-26
  • Contact: Zhihai WANG E-mail:16120396@bjtu.edu.cn;zhhwang@bjtu.edu.cn
  • Supported by:
    北京市自然科学基金(4182052);国家自然科学基金(61672086);国家自然科学基金(61702030);国家自然科学基金(61771058)

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Abstract:

Due to most of the existing data stream ensemble classification algorithms without considering the importance of historical data in the evaluation of the base classifier, while ignoring the treatment of interference with irrelevant attributes and noise attributes, an adaptive ensemble classification method based on deep attribute weighting for data stream (EMDAW) was proposed to effectively combine multiple naive Bayesian models based on depth attribute weighting. In different data blocks, the contribution of different attribute values to the attribution of class attributes was deeply analyzed, and the learned local attribute weights to different attribute values were applied to reduce noise data interference. In the evaluation of the base classifier, the importance of the historical data and the current latest data was weighed. The sub-classifier combination was used to improve the overall classification performance by using the combined voting strategy based on the test case classifier confidence and classification correct rate. By comparing experiments with classical algorithms on multiple benchmark datasets, the proposed algorithm had certain advantages in classification correct rate and concept drift adaptability.

Key words: data stream, ensemble classification, deep attribute weighting, concept drift, adaptive

CLC Number: 

  • TP391

Fig.1

Different types of concept drift"

Fig.2

Structure of attribute weighted na?ve Bayes"

Fig.3

Algorithm framework of EMDAW"

Table 1

The characters of several datasets"

数据集 实例数 属性数目 类标数 噪声比例/% 漂移数 漂移类型
HYP 1 000 000 10 2 5 1 增量式漂移
SEA 1 000 000 3 4 10 9 突变漂移
LEDM 1 000 000 24 10 10 3 混合漂移
LEDND 1 000 000 24 10 20 0 无
Cover type 581 000 53 7 未知
Electricity 45 000 7 2 未知
Poker 1 000 000 10 10 未知
Spam 9 342 500 2 未知

Table 2

Comparison of classification correct rates of several base classifiers"

%
分类器模型 HYP SEAF LEDm LEDnd Cover type Electricity Poker Spam
DAW 72.34 84.34 67.44 51.57 82.36 79.12 83.44 84.25
NB 77.48 84.86 67.14 51.27 66.04 77.88 59.46 80.25
HOT 75.46 85.78 67.22 51.13 74.93 77.12 83.36 78.09

Fig.4

The classification correct rate of different algorithms under different number of ensemble classifiers"

Fig.5

Average classification correct rate of the proposed algorithm under different parameters"

Table 3

The classification correct rate of different datasets with different data chunks"

%
数据集 数据块大小
500 750 1 000 1 250 1 500 1 750 2 000
HYP 84.27 85.39 85.97 86.19 86.29 86.60 86.59
SEAF 82.80 83.56 84.25 84.95 85.19 85.44 85.53
Electricity 77.14 77.27 79.33 78.69 78.12 78.50 78.51
Cover type 83.47 83.93 84.25 81.76 81.58 81.03 79.15

Table 4

The classification correct rate of each data set under different values ofparameter k with the ensemble strategy"

%
数据集 50 100 150 200
HYP 84.33 85.97 85.50 85.52
SEAF 83.80 84.71 84.83 84.33
Electricity 77.35 79.35 78.53 78.62
Cover type 84.04 84.25 84.02 83.84

Table 5

Average chunk training time of different classification algorithms"

ms
数据集 AWE AUE2 DDM NB Oza DWM NSE EMDAW
HYP 239.1 156.2 1 333.6 0.2 104.5 4 384.2 331.6 460.4
SEAF 87.0 42.1 32.1 0.1 378.8 1 246.2 262.5 358.1
LEDM 230.1 150.3 101.3 0.2 124.6 108.6 534.6 125.1
LEDND 230.6 150.6 120.2 0.2 132.6 120.5 834.6 142.3
Cover type 296.6 133.2 349.4 0.8 447.3 63.5 640.0 260.9
Electricity 290.1 180.6 85.3 0.4 408.8 40.2 173.3 318.6
Poker 173.5 155.7 42.8 0.2 314.6 49.2 762.8 544.4
Spam 750.6 669.5 191.6 3.6 810.5 24.6 152.2 197.9

Table 6

Average classification correct rate of different classification algorithms"

%
数据集 AWE AUE2 DDM NB Oza DWM NSE EMDAW
HYP 82.45 83.54 76.54 77.48 83.05 82.93 84.43 85.97
SEAF 84.05 86.77 84.95 84.86 85.04 85.38 83.01 84.71
LEDM 67.08 67.58 66.70 67.14 67.55 67.12 62.86 67.21
LEDND 51.27 51.26 51.18 51.27 51.23 51.26 47.16 50.57
Cover type 81.70 84.05 74.36 66.04 80.52 77.29 79.70 84.25
Electricity 77.67 78.21 76.25 77.88 77.34 76.69 76.70 79.35
Poker 53.87 66.88 62.14 59.46 65.19 60.72 53.73 62.60
Spam 74.86 72.23 77.25 80.25 78.92 80.26 68.78 81.37

Fig.6

The algorithms average classification correct rate ofdifferent chunksize"

Fig.7

The classification correct rate on the SEA dataset"

Fig.8

The classification correct rate on the HYP dataset"

Fig.9

The classification correct rate on the Electricity dataset"

Fig.10

The classification correct rate on the LEDm dataset"

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