Optimality driven nearest centroid classification from genomic data. Academic Article uri icon


  • Nearest-centroid classifiers have recently been successfully employed in high-dimensional applications, such as in genomics. A necessary step when building a classifier for high-dimensional data is feature selection. Feature selection is frequently carried out by computing univariate scores for each feature individually, without consideration for how a subset of features performs as a whole. We introduce a new feature selection approach for high-dimensional nearest centroid classifiers that instead is based on the theoretically optimal choice of a given number of features, which we determine directly here. This allows us to develop a new greedy algorithm to estimate this optimal nearest-centroid classifier with a given number of features. In addition, whereas the centroids are usually formed from maximum likelihood estimates, we investigate the applicability of high-dimensional shrinkage estimates of centroids. We apply the proposed method to clinical classification based on gene-expression microarrays, demonstrating that the proposed method can outperform existing nearest centroid classifiers.

published proceedings

  • PLoS One

altmetric score

  • 5.12

author list (cited authors)

  • Dabney, A. R., & Storey, J. D.

citation count

  • 28

complete list of authors

  • Dabney, Alan R||Storey, John D

editor list (cited editors)

  • Zhu, J. i.

publication date

  • January 2007