Abstract
In many application data are imperfect, imprecise or more generally uncertain. Many classification methods have been presented that can handle data in some parts of the learning or the inference process, yet seldom in the whole process. Also, most of the proposed approach still evaluate their results on precisely known data. However, there are no reason to assume the existence of such data in applications, hence the need for assessment method working for uncertain data. We propose such an approach here, and apply it to the pruning of E2M decision trees. This results in an approach that can handle data uncertainty wherever it is, be it in input or output variables, in training or in test samples.
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
Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees (1984)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the royal statistial society, series B 39(1), 1–38 (1977)
Denœux, T.: Maximum likelihood estimation from uncertain data in the belief function framework. IEEE Trans. on Know. and Data Eng. (2011)
Esposito, F., Malerba, D., Semeraro, G., Kay, J.: A comparative analysis of methods for pruning decision trees. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(5), 476–491 (1997)
Masson, M.H., Denoeux, T.: Ecm: An evidential version of the fuzzy c-means algorithm. Pattern Recognition 41(4), 1384–1397 (2008)
Périnel, E.: Construire un arbre de discrimination binaire à partir de données imprécises. Revue de statistique appliquée 4747, 5–30 (1999)
Smets, P.: Belief induced by the partial knowledge of the probabilities. In: Proceedings of the Tenth International Conference on Uncertainty in Artificial Intelligence, UAI 1994, pp. 523–530. Morgan Kaufmann Publishers Inc, San Francisco (1994)
Sutton-Charani, N., Destercke, S., Denœux, T.: Learning decision trees from uncertain data with an evidential em approach. In: 12th International Conference on Machine Learning and Applications, ICMLA (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Sutton-Charani, N., Destercke, S., Denœux, T. (2014). Training and Evaluating Classifiers from Evidential Data: Application to E2M Decision Tree Pruning. In: Cuzzolin, F. (eds) Belief Functions: Theory and Applications. BELIEF 2014. Lecture Notes in Computer Science(), vol 8764. Springer, Cham. https://doi.org/10.1007/978-3-319-11191-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-11191-9_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11190-2
Online ISBN: 978-3-319-11191-9
eBook Packages: Computer ScienceComputer Science (R0)