Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (4): 8-13.doi: 10.6040/j.issn.1672-3961.0.2019.050

• Machine Learning & Data Mining • Previous Articles     Next Articles

An ensemble learning algorithm for unbalanced data classification

Zongtang ZHANG1(),Sen WANG2,Shilin SUN1   

  1. 1. Navigation and Observation Department, Navy Submarine Academy, Qingdao 266000, Shandong, China
    2. 91154 force, Sanya 572000, Hainan, China
  • Received:2019-01-30 Online:2019-08-20 Published:2019-08-06

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Abstract:

For unbalanced data classification problem in underwater acoustic target recognition, a random subspace AdaBoost algorithm called RSBoost was proposed. Subtraining sample set was extracted by random subspace method in different underwater acoustic feature space and base classifier was trained in every subtraining sample set. The base classifier with the maximum margin mean of minority class was chosen as the base classifier of this round, the final ensemble classifier was formed iteratively. The experiment was carried out on the measured data, the performance of RSBoost and AdaBoost in different feature space was evaluated by F-measure and G-mean. The results showed that, compared with AdaBoost, the F-measure of RSBoost improved from 0.07 to 0.22 and the G-mean improved from 0.18 to 0.25, which showed that RSBoost was superior to AdaBoost in underwater acoustic unbalanced classification problem.

Key words: unbalanced data, ensemble learning, underwater acoustic target recognition, AdaBoost algorithm, random space

CLC Number: 

  • TP391

Fig.1

Flow chart of RSBoost algorithm"

Table 1

Confusion matrix for bi-class problem"

类别 预测少类 预测多类
实际少类 TP FN
实际多类 FP TN

Table 2

Feature dimension of data"

特征 Demon谱 功率谱 高阶谱 MFCC 小波
维度 512 14 19 36 16

Fig.2

F-measure of two algorithms"

Fig.3

G-mean of two algorithms"

Table 3

Performance of two algorithm in different feature space"

特征集 F-measure G-mean
RSBoost AdaBoost RSBoost AdaBoost
Demon 0.30 0.14 0.22 0.29
功率谱 0.18 0.06 0.26 0.17
高阶谱 0.18 0.04 0.18 0.11
MFCC 0.22 0.05 0.31 0.14
小波 0.21 0.07 0.26 0.18
平均 0.22 0.07 0.25 0.18
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