Using extreme prior probabilities on the Naive Credal Classifier Moral García, Serafín García Castellano, Francisco Javier Mantas Ruiz, Carlos Javier Abellán Mulero, Joaquín Imprecise classification Naive Credal Classifier Extreme Prior Naive Credal Classifier Lower and upper prior probabilities Non-dominated states set The Naive Credal Classifier (NCC) was the first method proposed for Imprecise Classification. It starts from the known Naive Bayes algorithm (NB), which assumes that the attributes are independent given the class variable. Despite this unrealistic assumption, NB and NCC have been successfully used in practical applications. In this work, we propose a new version of NCC, called Extreme Prior Naive Credal Classifier (EP-NCC). Unlike NCC, EP-NCC takes into consideration the lower and upper prior probabilities of the class variable in the estimation of the lower and upper conditional probabilities. We demonstrate that, with our proposed EP-NCC, the predictions are more informative than with NCC without increasing the risk of making erroneous predictions. An experimental analysis carried out in this work shows that EP-NCC significantly outperforms NCC and obtains statistically equivalent results to the algorithm proposed so far for Imprecise Classification based on decision trees, even though EP-NCC is computationally simpler. Therefore, EP-NCC is more suitable to be applied to large datasets for Imprecise Classification than the methods proposed so far in this field. This is an important issue in favor of our proposal due to the increasing amount of data in every area. 2024-02-07T10:10:10Z 2024-02-07T10:10:10Z 2022-02-15 journal article Moral-García, S., Castellano, J. G., Mantas, C. J., & Abellán, J. (2022). Using extreme prior probabilities on the Naive Credal Classifier. Knowledge-Based Systems, 107707. Knowledge-Based Systems. Doi: 10.1016/j.knosys.2021.107707 1872-7409 https://hdl.handle.net/10481/88529 10.1016/j.knosys.2021.107707 eng embargoed access Elsevier