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Bioinformatics Advance Access originally published online on August 25, 2006
Bioinformatics 2006 22(21):2597-2603; doi:10.1093/bioinformatics/btl458
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Weighted quality estimates in machine learning

Levon Budagyan 1,* and Ruben Abagyan 2

1 Molsoft LLC, 3366 North Torrey Pines Court Suite 300, San Diego, CA 92037, USA
2 The Scripps Research Institute 10550 North Torrey Pines Road, Mail TPC-28, San Diego, CA 92037, USA

*To whom correspondence should be addressed

Motivation: Machine learning methods such as neural networks, support vector machines, and other classification and regression methods rely on iterative optimization of the model quality in the space of the parameters of the method. Model quality measures (accuracies, correlations, etc.) are frequently overly optimistic because the training sets are dominated by particular families and subfamilies. To overcome the bias, the dataset is usually reduced by filtering out closely related objects. However, such filtering uses fixed similarity thresholds and ignores a part of the training information.

Results: We suggested a novel approach to calculate prediction model quality based on assigning to each data point inverse density weights derived from the postulated distance metric. We demonstrated that our new weighted measures estimate the model generalization better and are consistent with the machine learning theory. The Vapnik–Chervonenkis theorem was reformulated and applied to derive the space-uniform error estimates. Two examples were used to illustrate the advantages of the inverse density weighting. First, we demonstrated on a set with a built-in bias that the unweighted cross-validation procedure leads to an overly optimistic quality estimate, while the density-weighted quality estimates are more realistic. Second, an analytical equation for weighted quality estimates was used to derive an SVM model for signal peptide prediction using a full set of known signal peptides, instead of the usual filtered subset.

Contact: levon{at}molsoft.com

Received on March 7, 2006; revised on August 9, 2006; accepted on August 22, 2006

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