@misc{10481/88260,
year = {2018},
month = {2},
url = {https://hdl.handle.net/10481/88260},
abstract = {The application of classifiers on data represents an important help in a process of decision making. Any classifier, or other method used for knowledge extraction, suffers a deterioration when it is applied on data with noise. Credal C4.5 (CC4.5) is a recent method of classification, that introduces imprecise probabilities in the algorithm of the classic C4.5. It is very suitable in classification noise tasks, but it has a clear dependency of a parameter. It has been proved that this parameter is related with the level of overfitting of the model on the data used for training. In noisy domains, this characteristic is important in the sense that variations of this parameter can reduce the variance of the model. Depending on the degree of noise that a data set has, the application of different values of this parameter can produce different performance of the CC4.5 model. Hence, the use of the correct parameter is fundamental to attain a high level of performance for this model. In this paper, that problem is solved via a rough procedure to estimate the level of class noise in the training data. Combining this new noise estimation process with the CC4.5, it is presented a direct method that has an equivalent performance than the one of the CC4.5 when it is used with the best value of its parameter for each level of class noise.},
organization = {This work has been supported by the Spanish “Ministerio de Economía y Competitividad” and by “Fondo Europeo de Desarrollo Regional” (FEDER) under Project TEC2015-69496-R.},
publisher = {Elsevier},
keywords = {Classification},
keywords = {Decision trees},
keywords = {Imprecise probabilities},
keywords = {Uncertainty measures},
keywords = {Noisy data},
title = {AdaptativeCC4. 5: Credal C4. 5 with a rough class noise estimator},
doi = {10.1016/j.eswa.2017.09.057},
author = {Abellán Mulero, Joaquín and Mantas Ruiz, Carlos Javier and García Castellano, Francisco Javier},
}