Improved shrunken centroid classifiers for high-dimensional class-imbalanced data

@inproceedings{Blagus2012ImprovedSC,
  title={Improved shrunken centroid classifiers for high-dimensional class-imbalanced data},
  author={Rok Blagus and Lara Lusa},
  booktitle={BMC Bioinformatics},
  year={2012}
}
PAM, a nearest shrunken centroid method (NSC), is a popular classification method for high-dimensional data. ALP and AHP are NSC algorithms that were proposed to improve upon PAM. The NSC methods base their classification rules on shrunken centroids; in practice the amount of shrinkage is estimated minimizing the overall cross-validated (CV) error rate. We show that when data are class-imbalanced the three NSC classifiers are biased towards the majority class. The bias is larger when the number… CONTINUE READING
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