Tackling the problem of classification with noisy data using Multiple Classifier Systems: Analysis of the performance and robustness-Reference-Cited by-同舟云学术

Tackling the problem of classification with noisy data using Multiple Classifier Systems: Analysis of the performance and robustness

Published:2013-10 Issue: Volume:247 Page:1-20
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Sáez José A.,Galar Mikel,Luengo Julián,Herrera Francisco

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference54 articles.

1. KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework;Alcalá-Fdez;Journal of Multiple-Valued Logic and Soft Computing,2011

2. KEEL: a software tool to assess evolutionary algorithms for data mining problems;Alcalá-Fdez;Soft Computing-A Fusion of Foundations, Methodologies and Applications,2009

3. Class noise detection based on software metrics and ROC curves;Catal;Information Sciences,2011

4. Fast effective rule induction;Cohen,1995

5. Support vector networks;Cortes;Machine Learning,1995

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