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Cost-conscious classifier ensembles

Published: 15 October 2005 Publication History

Abstract

Ensemble methods improve the classification accuracy at the expense of testing complexity, resulting in increased computational costs in real-world applications. Developing a utility-based framework, we construct two novel cost-conscious ensembles; the first one determines a subset of classifiers and the second dynamically selects a single classifier. Both ensembles successfully switch between classifiers according to the accuracy-cost trade-off of an application.

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Cited By

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  • (2012)Eigenclassifiers for combining correlated classifiersInformation Sciences: an International Journal10.1016/j.ins.2011.10.024187(109-120)Online publication date: 1-Mar-2012
  • (2011)Lightweight power aware and scalable movement monitoring for wearable computersProceedings of the 2nd Conference on Wireless Health10.1145/2077546.2077554(1-10)Online publication date: 10-Oct-2011
  • (2011)Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithmsComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2011.03.018104:3(443-451)Online publication date: 1-Dec-2011
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Published In

cover image Pattern Recognition Letters
Pattern Recognition Letters  Volume 26, Issue 14
15 October 2005
186 pages

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 15 October 2005

Author Tags

  1. Computational cost
  2. Ensemble techniques
  3. Selection
  4. Utility theory
  5. Voting

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Cited By

View all
  • (2012)Eigenclassifiers for combining correlated classifiersInformation Sciences: an International Journal10.1016/j.ins.2011.10.024187(109-120)Online publication date: 1-Mar-2012
  • (2011)Lightweight power aware and scalable movement monitoring for wearable computersProceedings of the 2nd Conference on Wireless Health10.1145/2077546.2077554(1-10)Online publication date: 10-Oct-2011
  • (2011)Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithmsComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2011.03.018104:3(443-451)Online publication date: 1-Dec-2011
  • (2010)Neuro-fuzzy-combiner: an effective multiple classifier systemInternational Journal of Knowledge Engineering and Soft Data Paradigms10.1504/IJKESDP.2010.0346782:2(107-129)Online publication date: 1-Aug-2010
  • (2010)Cost-conscious multiple kernel learningPattern Recognition Letters10.1016/j.patrec.2009.12.02731:9(959-965)Online publication date: 1-Jul-2010
  • (2009)Incremental construction of classifier and discriminant ensemblesInformation Sciences: an International Journal10.1016/j.ins.2008.12.024179:9(1298-1318)Online publication date: 1-Apr-2009
  • (2007)Test-Cost Sensitive Classification Based on Conditioned Loss FunctionsProceedings of the 18th European conference on Machine Learning10.1007/978-3-540-74958-5_52(551-558)Online publication date: 17-Sep-2007

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