Journal of Northeastern University Natural Science ›› 2018, Vol. 39 ›› Issue (6): 792-796.DOI: 10.12068/j.issn.1005-3026.2018.06.007

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Adaptive Random Sampling Algorithm Based on the Balance Maximization

DONG Li-yan1, WANG Yue-qun1, LI Yong-li2, ZHU Qi1   

  1. 1. College of Computer Science and Technology, Jilin University, Changchun 130012, China; 2. School of Computer Science and Technology, Northeast Normal University, Changchun 130117, China.
  • Received:2017-04-21 Revised:2017-04-21 Online:2018-06-15 Published:2018-06-22
  • Contact: DONG Li-yan
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Abstract: The problem on the classification algorithm of imbalanced datasets was analyzed. Common methods of balancing data, including improvement of datasets and the improved algorithm, were summarized. Then a novel algorithm called adaptive random sampling algorithm was put forward based on balance maximization. The classification effect of random forest algorithm was further optimized. Experiments show that the proposed algorithm performs well with the imbalanced data, the new data are fitted with the original data, and it could improve the ability of classifier to deal with the imbalanced data.

Key words: imbalanced dataset, balance maximization, random sampling, random forest, data preprocessing

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