Classification and Error Estimation for Discrete Data
- نوع فایل : کتاب
- زبان : انگلیسی
- مؤلف : Ulisses M. Braga-Neto*
- چاپ و سال / کشور: 2009
Description
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.
Current Genomics, 2009, 10, 446-462 Received on: February 19, 2009 - Revised on: March 17, 2009 - Accepted on: March 19, 2009
