2008 21st IEEE International Symposium on Computer-Based Medical Systems Incremental Learning and Decremented Characterization of Gene Expression Data Analysis June 17-June 19 ISBN: 978-0-7695-3165-6
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CBMS.2008.63
In this study, we present Incremental Learning and Decremented Characterization of Regularized Generalized Eigenvalue Classification (ILDC-ReGEC), a novel algorithm to train a generalized eigenvalue classifier with a substantially smaller subset of points and features of the original data. The proposed method provides a constructive way to understand the influence of new training data on an existing classification model and the grouping of features that determine the class of samples. The proposed algorithm is compared with other well known solutions. Experimental results are conducted on publicly available datasets and standard parameters are used for evaluation.
Index Terms:
Feature selection, binary classification, incremental learning
Citation:
Mario Rosario Guarracino, Salvatore Cuciniello, Davide Feminiano, "Incremental Learning and Decremented Characterization of Gene Expression Data Analysis," cbms, pp.203-208, 2008 21st IEEE International Symposium on Computer-Based Medical Systems, 2008 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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