Abstract
In this paper, we present a new method to enhance classification performance based on Boosting by introducing nonlinear discriminant analysis as feature selection. To reduce the dependency between hypotheses, each hypothesis is constructed in a different feature space formed by Kernel Discriminant Analysis (KDA). Then, these hypotheses are integrated based on AdaBoost. To conduct KDA in each Boosting iteration within realistic time, a new method of kernel selection is also proposed. Several experiments are carried out for the blood cell data and thyroid data to evaluate the proposed method. The result shows that it is almost the same as the best performance of Support Vector Machine without any time-consuming parameter search.
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Abe, S. (2005) Support vector machines for pattern classification. Springer
Baudat, G., Anouar, F. (2000) Generalized discriminant analysis using a kernel approach. Neural Computation, Vol. 12: 2385–2404
Freund, Y., Schapire, R. E. (1997) A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, Vol. 55, No. 1: 119–139
Juwei, L., Plataniotis, K. N., Venetsanopoulos, A. N. (2003) Boosting linear discriminant analysis for face recognition. IEEE Int. Conf. on Image Processing: 14–17
Schölkopf, B., Smola, A., Müller, K.-R. (1996) Nonlinear component analysis as a kernel eigenvalue problem. MPI Technical Report, No. 44
Murua, A. (2002) Upper bounds for error rates of linear combinations of classifiers. IEEE Trans, on Pattern Analysis and Machine Intelligence, Vol. 24, No. 5: 591–602
Rätsch, G., Onoda, T., Müller, K.-R. (2001) Soft margins for AdaBoost. Machine Learning, Vol. 42, No. 3: 287–320
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Kita, S., Maekawa, S., Ozawa, S., Abe, S. (2005). Boosting Kernel Discriminant Analysis with Adaptive Kernel Selection. In: Ribeiro, B., Albrecht, R.F., Dobnikar, A., Pearson, D.W., Steele, N.C. (eds) Adaptive and Natural Computing Algorithms. Springer, Vienna. https://doi.org/10.1007/3-211-27389-1_103
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DOI: https://doi.org/10.1007/3-211-27389-1_103
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-24934-5
Online ISBN: 978-3-211-27389-0
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