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

ABSTRACT:

Background

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.

Results

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 of variables or class-imbalance is larger and/or the differences between classes are smaller. To diminish the class-imbalance problem of the NSC classifiers we propose to estimate the amount of shrinkage by maximizing the CV geometric mean of the class-specific pred

SUBMITTER: Blagus R 

PROVIDER: S-EPMC3687811 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature