OligoIS: Scalable Instance Selection for Class-Imbalanced Data Sets

@article{GarcaPedrajas2013OligoISSI,
  title={OligoIS: Scalable Instance Selection for Class-Imbalanced Data Sets},
  author={Nicol{\'a}s Garc{\'i}a-Pedrajas and Javier P{\'e}rez-Rodr{\'i}guez and Aida de Haro-Garc{\'i}a},
  journal={IEEE Transactions on Cybernetics},
  year={2013},
  volume={43},
  pages={332-346}
}
In current research, an enormous amount of information is constantly being produced, which poses a challenge for data mining algorithms. Many of the problems in extremely active research areas, such as bioinformatics, security and intrusion detection, or text mining, share the following two features: large data sets and class-imbalanced distribution of samples. Although many methods have been proposed for dealing with class-imbalanced data sets, most of these methods are not scalable to the… CONTINUE READING

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