Optimality driven nearest centroid classification from genomic data.
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.
Dabney
Alan R
AR
Department of Statistics, Texas A&M University, College Station, Texas, United States of America. adabney@stat.tamu.edu
Storey
John D
JD
D000465:Algorithms; D002648:Child; D003627:Data Interpretation, Statistical; D016002:Discriminant Analysis; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D005821:Genetic Techniques; D023281:Genomics; D006801:Humans; D007938:Leukemia; D008223:Lymphoma / Q000235:genetics; D015233:Models, Statistical; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated