Abstract
Credal Decision Trees (CDTs) are algorithms to design classifiers based on imprecise probabilities and uncertainty measures. C4.5 and CDT procedures are combined in this paper. The new algorithm builds trees for solving classification problems assuming that the training set is not fully reliable. This algorithm is especially suitable to classify noisy data sets. This is shown in the experiments. © 2014 Springer International Publishing.
Cite
CITATION STYLE
Mantas, C. J., & Abellán, J. (2014). Credal decision trees to classify noisy data sets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8480 LNAI, pp. 689–696). Springer Verlag. https://doi.org/10.1007/978-3-319-07617-1_60
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