JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2018, Vol. 48 ›› Issue (3): 134-139.doi: 10.6040/j.issn.1672-3961.0.2017.416

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An over sampling algorithm based on clustering

WANG Huan, ZHOU Zhongmei   

  1. School of Computer, Minnan Normal University, Zhangzhou 363000, Fujian, China
  • Received:2017-08-24 Online:2018-06-20 Published:2017-08-24

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Abstract: In the research of over sampling, in order to generate meaningful new samples, the ClusteredSMOTE-Boost was proposed, which was based on the clustering technique. The algorithm filtered the noisy of minority class samples and took the remaining minority class samples as target samples to synthesize new samples. According to characteristics of the cluster of target samples after clustering determined the weight and the number of the target samples for the whole training set. All target samples were clustered and K-nearest neighbors in the cluster of the target sample were selected, and then a sample from K-nearest neighbors was randomly chosen to synthesize new sample with target sample. Thus, new samples were similar with samples in the target cluster. This method reduced the complexity of the boundary caused by the additional new samples. The experimental results showed that the ClusteredSMOTE-Boost algorithm was superior to the three classical algorithms SMOTE-Boost, ADASYN-Boost, BorderlineSMOTE-Boost on the variety of measures.

Key words: over sampling, instance weights, classification, cluster, imbalanced data

CLC Number: 

  • TP311
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