title: Some considerations of classification for high dimension low-sample size data.
abstract: We review in this article several classification methods, especially for high-dimensional and low-sample size data. We discuss several desirable properties for classifiers in such settings, including predictability, consistency, generality, stability, robustness and sparsity. Specifically, a good classifier should have a small prediction error (predictability); converge to the Bayes-rule classifier asymptotically (consistency); be stable when adding/removing an observation (generality); be stable for different data sets of the same kind (stochastic stability); be stable when there are a small number of contaminated observations (robustness); and have a small number of variables in the classifier (interpretability or sparsity). Several simulation examples and real applications are used to illustrate the usefulness of the existing popular classifiers and compare their performance.
authors: 1Department of Statistics, Purdue University, West Lafayette, IN, USA.
authors: Lingsong
authors: L
authors: Zhang
authors: Xihong
authors: X
authors: Lin
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D008297:Male; D011471:Prostatic Neoplasms / Q000473:pathology; D018401:Sample Size; D060388:Support Vector Machine