A new under-sampling method to face class overlap and imbalance

HIGHLIGHTS

• who: A. Guzmu00e1n-Ponce and collaborators from the Facultad de Ingenieru00eda, Universidad Autu00f3noma del Estado de Mexico, Cerro de Coatepec s/n have published the research: A New Under-Sampling Method to Face Class Overlap and Imbalance, in the Journal: (JOURNAL)
• what: The authors propose a two-stage under-sampling technique that combines the DBSCAN clustering algorithm to remove noisy samples and clean the decision boundary with a minimum spanning tree algorithm to face the class imbalance thus handling class overlap and imbalance simultaneously with the aim of improving the performance of classifiers. It is . . .
  

   

  

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