Application of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority Voting
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.
KeywordsGenetic Algorithm Evolutionary Algorithm Tabu Search Majority Vote Probability Vector
Unable to display preview. Download preview PDF.
- 8.Kuncheva L.I., Whitaker C.J., Shipp C.A., Duin R.P.W.: Limits on the Majority Vote Accuracy in Classifier Fusion. Submitted to IEEE Transactions on Pattern Analysis and Machine IntelligenceGoogle Scholar
- 9.Ruta D., Gabrys B.: A Theoretical Analysis of the Limits of Majority Voting in Multiple Classifier Systems. Technical Report No. 11. University of Paisley (2000)Google Scholar
- 10.Kuncheva L.I., Whitaker C.J.: Measures of Diversity in Classifier Ensembles. Submitted to Machine LearningGoogle Scholar
- 11.Ruta D., Gabrys B.: Analysis of the Correlation Between Majority Voting Errors and the Diversity Measures in Multiple Classifier Systems. Accepted for the International Symposium on Soft Computing SOCO’2001Google Scholar
- 12.Kuncheva L., Jain L.C.: Designing Classifier Fusion Systems by Genetic Algorithms. To appear in IEEE Transactions on Evolutionary ComputationGoogle Scholar
- 15.Kuncheva L.I., Bezdek J.C.: On Combining Classifiers by Fuzzy Templates. Proc. NAFIPS’98, Pensacola, FL (1998) 193–197Google Scholar
- 16.Davis L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold New York (1991)Google Scholar
- 18.Baluja S.: Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning. Technical Report No. 163. Carnegie Melon University, Pittsburgh PA (1994)Google Scholar