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.
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© 2014 Springer International Publishing Switzerland
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Mantas, C.J., Abellán, J. (2014). Credal Decision Trees to Classify Noisy Data Sets. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, JS., Woźniak, M., Quintian, H., Corchado, E. (eds) Hybrid Artificial Intelligence Systems. HAIS 2014. Lecture Notes in Computer Science(), vol 8480. Springer, Cham. https://doi.org/10.1007/978-3-319-07617-1_60
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DOI: https://doi.org/10.1007/978-3-319-07617-1_60
Publisher Name: Springer, Cham
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