MCS 2003: Multiple Classifier Systems pp 94-105 | Cite as
Towards Automated Classifier Combination for Pattern Recognition
Abstract
This study covers weighted combination methodologies for multiple classifiers to improve classification accuracy. The classifiers are extended to produce class probability estimates besides their class label assignments to be able to combine them more efficiently. The leave-one-out training method is used and the results are combined using proposed weighted combination algorithms. The weights of the classifiers for the weighted classifier combination are determined based on the performance of the classifiers on the training phase. The classifiers and combination algorithms are evaluated using classical and proposed performance measures. It is found that the integration of the proposed reliability measure, improves the performance of classification. A sensitivity analysis shows that the proposed polynomial weight assignment applied with probability based combination is robust to choose classifiers for the classifier set and indicates a typical one to three percent consistent improvement compared to a single best classifier of the same set.
Keywords
Class Label Combination Method Good Classifier Combination Algorithm Weight AssignmentPreview
Unable to display preview. Download preview PDF.
References
- 1.Alexandre, L., A. Campilho and M. Kamel, 2000, “Combining Unbiased and Independent Classifiers Using Weighted Average”, 11 th Portuguese Conference on Pattern Recognition, pp. 495–498, Porto, Portugal.Google Scholar
- 2.Baykut, A., 2002, “Classifier Combination Methods in Pattern Recognition”, PhD. Thesis, Bogaziçi University, Istanbul, Turkey.Google Scholar
- 3.Bauer, E. and R. Kohavi, 1999, “An Empirical Comparison of Voting Classification Algorithms: Bagging”, Boosting and Variants, Machine Learning, Vol. 36.Google Scholar
- 4.Christianini, N. and J. S. Taylor, 2000, Support Vector Machines and other kernel-based learning methods, Cambridge University Press, UK.Google Scholar
- 5.Cortes, C. and V. Vapnik, 1995, “Support Vector Networks”, Machine Learning, Vol. 20, pp. 273–297.MATHGoogle Scholar
- 6.Duda, R. O. and P. E. Hart, Stork, D.G. 2001, Pattern Classification, John Wiley&Sons.Google Scholar
- 7.Kittler, J., 1998, “Combining Classifiers: A Theoretical Framework”, Pattern Analysis and Applications, Vol. 1, No. 1, pp. 18–28.CrossRefMathSciNetGoogle Scholar
- 8.Kittler, J., M. Hatef, R. P. W. Duin and J. Matas, 1998, “On Combining Classifiers”, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 20, No. 3, pp. 226–240.CrossRefGoogle Scholar
- 9.Shalkoff, R. J., 1992b, Pattern Recognition: Statistical, Structural and Neural Approaches, John Wiley&Sons.Google Scholar