Abstract
In this work, we present new ways of dealing with categorical attributes; in particular, the methodology introduced here concerns the use of these attributes in binary decision trees. We consider essentially two main operations; the first one consists in using the joint distribution of two or more categorical attributes in order to increase the final performance of the decision tree; the second - and the most important - operation, concerns the extraction of relatively few predictive binary attributes from a categorical attribute; specially, when the latter has a large number of values. With more than two classes to predict, most of the present binary decision tree software needs to test an exponential number of binary attributes for each categorical attribute; which can be prohibitive. Our method, ARCADE, is independent of the number of classes to be predicted, and it starts by reducing significantly the number of values of the initial categorical attribute. This is done by clustering the initial values, using a hierarchical classification method. Each cluster of values will then represent a new value of a new categorical attribute. This new attribute will then be used in the decision tree, instead of the initial one. Nevertheless, not all of the binary attributes associated with this new categorical attribute will be used; only those which are predictive. The reduction in the complexity of the search for the best binary split is therefore enormous, as will be seen in the application that we consider; that is, the old and still lively protein secondary structure prediction problem.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Almuallim, H., Akiba, Y. & Kaneda, S. (1995). On Handling Tree-Structured Attributes in Decision Tree Learning. International Conference on Machine Learning, 1995.
Asseraf, M. (1996). Extension de la distance de Kolmogorov-Smirnov et stratégie de la coupure optimale. IVème journées de la société francophone de classification (SFC), Vannes, France. Breiman, L., Friedman, J., Olshen, A. & Stone, C. (1984) Classification and Regression Trees. Wadsworth.
Colloc’h, N., Etchebest, C., Thoreau, E., Henrissat, B., & Mornon, J. (1993). Protein Engineering, 6, 377–382.
Lerman. I.C. (1993). Likelihood linkage analysis (LLA) classification method: An example treated by hand. Biochimie, Elsevier editions, volume 75, pp. 379–397.
Lerman, I.C. & Pinto da Costa, J. (1996). Variables à très grand nombre de catégories dans les arbres de décision. Application à Videntification de la structure secondaire d’une protéine. Rapport de Recherche Inria, 2803, 46 pages.
Pinto da Costa, J. (1996). Coefficients d’association et binarisation par la classification hiérarchique dans les arbres de décision. Application à l’identification de la structure secondaire des protéines. Thèse de l’Université de Rennes I.
Quinlan, J.R. (1993). C4. 5: Programs for Machine Learning. Morgan Kaufman, California.
Rost, B. & Sander, C. (1993). Prediction of protein secondary structures at better than 70%. J. Mol. Biology, 232, pp. 584–599.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin · Heidelberg
About this paper
Cite this paper
Lerman, I.C., Pinto da Costa, J.F. (1998). How to extract predictive binary attributes from a categorical one. In: Rizzi, A., Vichi, M., Bock, HH. (eds) Advances in Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72253-0_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-72253-0_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64641-9
Online ISBN: 978-3-642-72253-0
eBook Packages: Springer Book Archive