Bioinformatics Advance Access originally published online on July 4, 2006
Bioinformatics 2006 22(17):2114-2121; doi:10.1093/bioinformatics/btl346
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Detecting potential labeling errors in microarrays by data perturbation
1 Department of Information and Communication Technology, University of Trento 38050 Povo, Italy
2 Department of Computer Science, University of British Columbia Vancouver, BC V6T1Z4, Canada
*To whom correspondence should be addressed.
Motivation: Classification is widely used in medical applications. However, the quality of the classifier depends critically on the accurate labeling of the training data. But for many medical applications, labeling a sample or grading a biopsy can be subjective. Existing studies confirm this phenomenon and show that even a very small number of mislabeled samples could deeply degrade the performance of the obtained classifier, particularly when the sample size is small. The problem we address in this paper is to develop a method for automatically detecting samples that are possibly mislabeled.
Results: We propose two algorithms, a classification-stability algorithm and a leave-one-out-error-sensitivity algorithm for detecting possibly mislabeled samples. For both algorithms, the key structure is the computation of the leave-one-out perturbation matrix. The classification-stability algorithm is based on measuring the stability of the label of a sample with respect to label changes of other samples and the version of this algorithm based on the support vector machine appears to be quite accurate for three real datasets. The suspect list produced by the version is of high quality. Furthermore, when human intervention is not available, the correction heuristic appears to be beneficial.
Contact: malossin{at}dit.unitn.it
Received on November 28, 2005; revised on May 28, 2006; accepted on June 22, 2006
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