Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (1): 94-99.doi: 10.11896／j.issn.1002-137X.2019.01.014

• CCDM2018 • Previous Articles     Next Articles

Sample Adaptive Classifier for Imbalanced Data

CAI Zi-xin, WANG Xin-yue, XU Jian, JING Li-ping   

  1. (Beijing Key Lab of Traffic Data Analysis and Mining,Beijing Jiaotong University,Beijing 100044,China)
  • Received:2018-04-23 Online:2019-01-15 Published:2019-02-25

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Abstract: In the era of big data,the imbalanced data is ubiquitous and inevitable,which has been a critical classification issue.Taking binary classification as an example,traditional learning algorithms can not sufficiently learn the hidden patterns from the minority class and may be biased towards majority class.To solve this problem,an effective way is using the cost-sensitive learning to improve the performance of prediction for the minority class which assigns ahighercost to misclassification of the minority.However,these methods equally treat the instances within one class.Actually,different instances may make different contributions to learning process.In order to make the cost-sensitive learning more effective,this paper proposed a sample-adaptive and cost-sensitive strategy for the classification of imbalanced data,which assigns a different weight to every single instance if misclassification occurs.Firstly,the strategy determines the distances between the boundary and instances according to the local distribution of the instances.Then,it assigns higher weights to the instances nearer to the boundary on the top of the margin theory.In this paper,the proposed strategy was applied to the classical LDM method.And a series of experiments on the UCI datasets prove that the sample-adaptive and cost-sensitive strategy can effectively improve the classifier’s performance on imbalanced data classification.

Key words: Boundary sample, Classification, Cost-sensitive learning

CLC Number: 

  • TP391
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