Abstract
In this paper four local classification methods are described and their statistical properties in the case of local data generating processes (LDGPs) are compared. In order to systematically compare the local methods and LDA as global standard technique, they are applied to a variety of situations which are simulated by experimental design. This way, it is possible to identify characteristics of the data that influence the classification performances of individual methods. For the simulated data sets the local methods on the average yield lower error rates than LDA. Additionally, based on the estimated effects of the influencing factors, groups of similar methods are found and the differences between these groups are revealed. Furthermore, it is possible to recommend certain methods for special data structures.
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References
CZOGIEL, I., LUEBKE, K., ZENTGRAF, M. and WEIHS, C. (2006): Localized Linear Discriminant Analysis. In: R. Decker, H.-J. Lenz (Eds.): Advances in Data Analysis. Springer, Berlin, 133-140.
HASTIE, T. J. and TIBSHIRANI, R. J. (1996): Discriminant Analysis by Gaussian Mixtures. Journal of the Royal Statistical Society B, 58, 155-176.
JOHNSON, N. L. (1949): Systems of Frequency Curves generated by Methods of Translation. Biometrika, 36, 149-176.
MILLER, D. J. and UYAR, H. S. (1998): Combined Learning and Use for a Mixture Model Equivalent to the RBF Classifier. Neural Computation, 10, 281-293.
SCHIFFNER, J. (2006): Vergleich von Klassifikationsverfahren für lokale Modelle. Diploma Thesis, Department of Statistics, University of Dortmund, Dortmund, Germany.
TITSIAS, M. K. and LIKAS, A. (2001): Shared Kernel Models for Class Conditional Density Estimation. IEEE Transactions on Neural Networks, 12(5), 987-997.
TITSIAS, M. K. and LIKAS, A. (2002): Mixtures of Experts Classification Using a Hierarchi-cal Mixture Model. Neural Computation, 14, 2221-2244.
TUTZ, G. and BINDER, H. (2005): Localized Classification. Statistics and Computing, 15, 155-166.
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Schiffner, J., Weihs, C. (2008). Comparison of Local Classification Methods. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78246-9_9
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DOI: https://doi.org/10.1007/978-3-540-78246-9_9
Publisher Name: Springer, Berlin, Heidelberg
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