75 research outputs found
Using extreme prior probabilities on the Naive Credal Classifier
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.This work has been supported by UGR-FEDER funds under Project A-TIC-344-UGR20, by the “FEDER/Junta de AndalucĂa-ConsejerĂa de TransformaciĂłn EconĂłmica, Industria, Conocimiento y Universidades ” under Project P20_00159, and by research scholarship FPU17/02685
Bagging of Credal Decision Trees for Imprecise Classification
The Credal Decision Trees (CDT) have been adapted for Imprecise Classification (ICDT). However, no ensembles of imprecise classifiers have been proposed so far. The reason might be that it is not a trivial question to combine the predictions made by multiple imprecise classifier. In fact, if the combination method used is not appropriate, the ensemble method could even worse the performance of one single classifier. On the other hand, the Bagging scheme has shown to provide satisfactory results in precise classification, specially when it is used with CDTs, which are known to be very weak and unstable classifiers. For these reasons, in this research, it is proposed a new Bagging scheme with ICDTs. It is presented a new technique for combining predictions made by imprecise classifiers that tries to maximize the precision of the bagging classifier. If the procedure for such a combination is too conservative it is easy to obtain few information and worse the results of a single classifier. Our proposal considers only the states with the minimum level of non-dominance. An exhaustive experimentation carried out in this work has shown that the Bagging of ICDTs, with our proposed combination technique, performs clearly better than a single ICDT.This work has been supported by the Spanish “Ministerio de EconomĂa y Competitividad” and by “Fondo Europeo de Desarrollo Regional” (FEDER) under Project TEC2015-69496-R
Upgrading the Fusion of Imprecise Classifiers
Imprecise classification is a relatively new task within Machine Learning. The difference
with standard classification is that not only is one state of the variable under study determined, a set
of states that do not have enough information against them and cannot be ruled out is determined
as well. For imprecise classification, a mode called an Imprecise Credal Decision Tree (ICDT) that
uses imprecise probabilities and maximum of entropy as the information measure has been presented.
A difficult and interesting task is to show how to combine this type of imprecise classifiers.
A procedure based on the minimum level of dominance has been presented; though it represents a
very strong method of combining, it has the drawback of an important risk of possible erroneous
prediction. In this research, we use the second-best theory to argue that the aforementioned type of
combination can be improved through a new procedure built by relaxing the constraints. The new
procedure is compared with the original one in an experimental study on a large set of datasets, and
shows improvement.UGR-FEDER funds under Project A-TIC-344-UGR20FEDER/Junta de AndalucĂa-ConsejerĂa de TransformaciĂłn EconĂłmica, Industria, Conocimiento
y Universidades” under Project P20_0015
Robust Statistical Comparison of Random Variables with Locally Varying Scale of Measurement
Spaces with locally varying scale of measurement, like multidimensional
structures with differently scaled dimensions, are pretty common in statistics
and machine learning. Nevertheless, it is still understood as an open question
how to exploit the entire information encoded in them properly. We address this
problem by considering an order based on (sets of) expectations of random
variables mapping into such non-standard spaces. This order contains stochastic
dominance and expectation order as extreme cases when no, or respectively
perfect, cardinal structure is given. We derive a (regularized) statistical
test for our proposed generalized stochastic dominance (GSD) order,
operationalize it by linear optimization, and robustify it by imprecise
probability models. Our findings are illustrated with data from
multidimensional poverty measurement, finance, and medicine.Comment: Accepted for the 39th Conference on Uncertainty in Artificial
Intelligence (UAI 2023
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