An imbalanced data classification method based on automatic clustering under-sampling

2016 ◽  
Author(s):  
Xiaoheng Deng ◽  
Weijian Zhong ◽  
Ju Ren ◽  
Detian Zeng ◽  
Honggang Zhang
Keyword(s):  
Imbalanced Data ◽  
Data Classification ◽  
Classification Method ◽  
Automatic Clustering ◽  
Imbalanced Data Classification ◽  
Under Sampling

Imbalanced Data Classification Based on Clustering

2013 ◽  
Vol 443 ◽  
pp. 741-745
Author(s):  
Hu Li ◽  
Peng Zou ◽  
Wei Hong Han ◽  
Rong Ze Xia
Keyword(s):  
Real World ◽  
Imbalanced Data ◽  
Data Classification ◽  
Comprehensive Analysis ◽  
Classification Method ◽  
Classification Methods ◽  
Real World Data ◽  
Minority Class ◽  
Imbalanced Data Classification ◽  
Traditional Classification

Many real world data is imbalanced, i.e. one category contains significantly more samples than other categories. Traditional classification methods take different categories equally and are often ineffective. Based on the comprehensive analysis of existing researches, we propose a new imbalanced data classification method based on clustering. The method clusters both majority class and minority class at first. Then, clustered minority class will be over-sampled by SMOTE while clustered majority class be under-sampled randomly. Through clustering, the proposed method can avoid the loss of useful information while resampling. Experiments on several UCI datasets show that the proposed method can effectively improve the classification results on imbalanced data.

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