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Smart Under-Sampling for the Detection of Rare Patterns in Unbalanced Datasets

  • Marco VannucciEmail author
  • Valentina Colla
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 56)

Abstract

A novel resampling approach is presented which improves the performance of classifiers when coping with unbalanced datasets. The method selects the frequent samples, whose elimination from the training dataset is most beneficial, and automatically determines the optimal unbalance rate. The results achieved test datasets put into evidence the efficiency of the method, that allows a sensible increase of the rare patterns detection rate and an improvement of the classification performance.

Keywords

False Alarm Training Dataset Frequent Pattern Minority Class Class Imbalance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.TeCIP Institute, Scuola Superiore Sant’AnnaPisaItaly

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