Multiple-rule bias in the comparison of classification rules.

ABSTRACT:

Motivation

There is growing discussion in the bioinformatics community concerning overoptimism of reported results. Two approaches contributing to overoptimism in classification are (i) the reporting of results on datasets for which a proposed classification rule performs well and (ii) the comparison of multiple classification rules on a single dataset that purports to show the advantage of a certain rule.

Results

This article provides a careful probabilistic analysis of the second issue and the 'multiple-rule bias', resulting from choosing a classification rule having minimum estimated error on the dataset. It quantifies this bias corresponding to estimating the expected true error of the classification rule possessing minimum estimated error and it characterizes the bias f

SUBMITTER: Yousefi MR 

PROVIDER: S-EPMC3106200 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature