Application of the evolutionary algorithms for classifier selection in multiple classifier systems with majority voting
Authors: Ruta, D. and Gabrys, B.
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 2096
Pages: 399-408
eISSN: 1611-3349
ISBN: 9783540422846
ISSN: 0302-9743
DOI: 10.1007/3-540-48219-9_40
Abstract:In many pattern recognition tasks, an approach based on combining classifiers has shown a significant potential gain in comparison to the performance of an individual best classifier. This improvement turned out to be subject to a sufficient level of diversity exhibited among classifiers, which in general can be assumed as a selective property of classifier subsets. Given a large number of classifiers, an intelligent classifier selection process becomes a crucial issue of multiple classifier system design. In this paper, we have investigated three evolutionary optimization methods for the classifier selection task. Based on our previous studies of various diversity measures and their correlation with majority voting error we have adopted majority voting performance computed for the validation set directly as a fitness function guiding the search. To prevent from training data overfitting we extracted a population of best unique classifier combinations, and used them for second stage majority voting. In this work we intend to show empirically, that using efficient evolutionary-based selection leads to the results comparable to absolutely best, found exhaustively. Moreover, as we showed for selected datasets, introducing a second stage combining by majority voting has the potential for both, further improvement of the recognition rate and increase of the reliability of combined outputs.
Source: Scopus
Application of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority Voting
Authors: Ruta, D. and Gabrys, B.
Editors: Kittler, J. and Roli, F.
Pages: 399-408
Publisher: Springer Berlin / Heidelberg
Place of Publication: London
ISBN: 978-3-540-42284-6
DOI: 10.1007/3-540-48219-9_40
Abstract:In many pattern recognition tasks, an approach based on combining classifiers has shown a significant potential gain in comparison to the performance of an individual best classifier. This improvement turned out to be subject to a sufficient level of diversity exhibited among classifiers, which in general can be assumed as a selective property of classifier subsets. Given a large number of classifiers, an intelligent classifier selection process becomes a crucial issue of multiple classifier system design. In this paper, we have investigated three evolutionary optimization methods for the classifier selection task. Based on our previous studies of various diversity measures and their correlation with majority voting error we have adopted majority voting performance computed for the validation set directly as a fitness function guiding the search. To prevent from training data overfitting we extracted a population of best unique classifier combinations, and used them for second stage majority voting. In this work we intend to show empirically, that using efficient evolutionary-based selection leads to the results comparable to absolutely best, found exhaustively. Moreover, as we showed for selected datasets, introducing a second stage combining by majority voting has the potential for both, further improvement of the recognition rate and increase of the reliability of combined outputs.
http://www.springerlink.com/content/vwaycadawr2fcjv8/?p=dabdbe745320484d9b56fb79525cb888&pi=16
Source: Manual
Preferred by: Dymitr Ruta
Application of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority Voting.
Authors: Ruta, D. and Gabrys, B.
Editors: Kittler, J. and Roli, F.
Journal: Multiple Classifier Systems
Volume: 2096
Pages: 399-408
Publisher: Springer
https://doi.org/10.1007/3-540-48219-9
Source: DBLP