Credal ensembles of classifiers
نویسندگان
چکیده
منابع مشابه
Lazy Credal Classifier and how to compare credal classifiers
This poster carries out two main contributions: (a) a lazy (or local) version of naive credal classifier (NCC) that we call lazy naive credal classifier (LNCC); (b) two metrics to compare credal classifiers. NCC [1] has extended naive Bayes (NB) to imprecise probabilities, by modeling prior ignorance via the Imprecise Dirichlet Model; the classification is eventually issued by returning the non...
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Texture classification is used in a large number of fields, ranging from industrial applications to remote sensing and biomedical engineering. The problem is traditionally solved by first computing texture descriptors (i.e. feature vectors) from image data and then performing supervised or unsupervised classification. In this paper we adopt the Local Binary Patterns descriptor [3], and focus on...
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Predictions made by imprecise-probability models are often indeterminate (that is, set-valued). Measuring the quality of an indeterminate prediction by a single number is important to fairly compare different models, but a principled approach to this problem is currently missing. In this paper we derive, from a set of assumptions, a metric to evaluate the predictions of credal classifiers. Thes...
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An ensemble of classifiers is a set of classifiers whose predictions are combined in some way to classify new instances. Early research has shown that, in general, an ensemble of classifiers is more accurate than any of the single classifiers in the ensemble. Usually the gains obtained by combining different classifiers are more affected by the chosen classifiers than by the used combination. I...
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ژورنال
عنوان ژورنال: Computational Statistics & Data Analysis
سال: 2014
ISSN: 0167-9473
DOI: 10.1016/j.csda.2012.11.010