Title: Aggregating classifiers with mathematical programming
Authors: Adem, Jan ×
Gochet, Willy #
Issue Date: Nov-2004
Publisher: Elsevier science bv
Series Title: Computational statistics & data analysis vol:47 issue:4 pages:791-807
Abstract: Bagging and boosting are popular and often successful ways to improve the performance of a classifier by means of aggregation. Classifiers can also be aggregated by means of an efficient and flexible mathematical programming model. This data-based approach guarantees that the aggregated classifier will be at least as good as the best predictor on the design data set for a user-defined criterion function. The mathematical programming approach is evaluated on real-world data sets from different contexts such as medical diagnosis, image segmentation and handwritten digit recognition. The real-world examples show that the approach can outperform both bagging and boosting. (C) 2003 Elsevier B.V. All rights reserved.
ISSN: 0167-9473
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Faculty of Economics and Business (FEB) - miscellaneous
Research Center for Operations Research and Business Statistics (ORSTAT), Leuven
× corresponding author
# (joint) last author

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