Classification and error estimation for discrete data. Academic Article uri icon

abstract

  • Discrete classification is common in Genomic Signal Processing applications, in particular in classification of discretized gene expression data, and in discrete gene expression prediction and the inference of boolean genomic regulatory networks. Once a discrete classifier is obtained from sample data, its performance must be evaluated through its classification error. In practice, error estimation methods must then be employed to obtain reliable estimates of the classification error based on the available data. Both classifier design and error estimation are complicated, in the case of Genomics, by the prevalence of small-sample data sets in such applications. This paper presents a broad review of the methodology of classification and error estimation for discrete data, in the context of Genomics, focusing on the study of performance in small sample scenarios, as well as asymptotic behavior.

published proceedings

  • Curr Genomics

author list (cited authors)

  • Braga-Neto, U. M.

citation count

  • 7

complete list of authors

  • Braga-Neto, Ulisses M

publication date

  • January 2009