Multiple-rule bias in the comparison of classification rules

• Published in: Bioinformatics (Oxford, England) Vol. 27; no. 12; pp. 1675 - 1683
• Main Authors: YOUSEFI, Mohammadmahdi R, JIANPING HUA, DOUGHERTY, Edward R
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 15.06.2011
• Subjects: Biological and medical sciences; Classification - methods; Computational Biology - methods; Fundamental and applied biological sciences. Psychology; General aspects; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Oligonucleotide Array Sequence Analysis; Original Papers; Multiple; Multiple comparison; Bias; Classification
• ISSN: 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/btr262

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. 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 from estimating the true comparative advantage of the chosen classification rule relative to the others by the estimated comparative advantage on the dataset. The analysis is applied to both synthetic and real data using a number of classification rules and error estimators. We have implemented in C code the synthetic data distribution model, classification rules, feature selection routines and error estimation methods. The code for multiple-rule analysis is implemented in MATLAB. The source code is available at http://gsp.tamu.edu/Publications/supplementary/yousefi11a/. Supplementary simulation results are also included.