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. 2014 Nov 18;15(1):378. doi: 10.1186/s12859-014-0378-y

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© Hu et al.; licensee BioMed Central Ltd. 2014

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Cascade generalization architecture. The conformational epitope predictors and linear epitope predictors all served at Level 0 as the base predictors. We placed k-NN, C4.5, ANN, and SVM sequentially from Levels 1 to 4 as meta learners. Each meta learner generalized the output from the previous level to meta knowledge in the form of meta features. The meta features and base features propagated sequentially to the successive level as input to the subsequent meta learner. The top-level meta learner, SVM, produced the final meta classification.