A Synthetic Minority Oversampling Method Based on Local Densities in Low-Dimensional Space for Imbalanced Learning

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• 作者：Zhipeng Xie (17) (18)
  Liyang Jiang (17)
  Tengju Ye (17)
  Xiaoli Li (19)
  
  17. School of Computer Science ; Fudan University ; Shanghai ; China
  18. Shanghai Key Laboratory of Data Science ; Fudan University ; Shanghai ; China
  19. Institute of InfoComm Research ; Fusionopolis Way ; Singapore ; Singapore
  
• 关键词：Imbalanced learning ; Oversampling method ; Local densities ; Dimensionality reduction
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9050
• 期：1
• 页码：3-18
• 全文大小：293 KB
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• 作者单位：Database Systems for Advanced Applications
• 丛书名：978-3-319-18122-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Imbalanced class distribution is a challenging problem in many real-life classification problems. Existing synthetic oversampling do suffer from the curse of dimensionality because they rely heavily on Euclidean distance. This paper proposed a new method, called Minority Oversampling Technique based on Local Densities in Low-Dimensional Space (or MOT2LD in short). MOT2LD first maps each training sample into a low-dimensional space, and makes clustering of their low-dimensional representations. It then assigns weight to each minority sample as the product of two quantities: local minority density and local majority count, indicating its importance of sampling. The synthetic minority class samples are generated inside some minority cluster. MOT2LD has been evaluated on 15 real-world data sets. The experimental results have shown that our method outperforms some other existing methods including SMOTE, Borderline-SMOTE, ADASYN, and MWMOTE, in terms of G-mean and F-measure.