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首页> 外文期刊>International journal of general systems >Application of uncertainty measures on credal sets on the naive Bayesian classifier
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Application of uncertainty measures on credal sets on the naive Bayesian classifier

机译：不确定性度量在朴素贝叶斯分类器上的集落数上的应用

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The naive Bayes classifier is known to obtain good results with a simple procedure. The method is based on the independence of the attribute variables given the variable to be classified. In real databases, where this hypothesis is not verified, this classifier continues to give good results. In order to improve the accuracy of the method, various works have been carried out in an attempt to reconstruct the set of the attributes and to join them so that there is independence between the new sets although the elements within each set are dependent. These methods are included in the ones known as semi-naive Bayes classifiers. In this article, we present an application of uncertainty measures on closed and convex sets of probability distributions, also called credal sets, in classification. We represent the information obtained from a database by a set of probability intervals (a credal set) via the imprecise Dirichlet model and we use uncertainty measures on credal sets in order to reconstruct the set of attributes, such as those mentioned, which shall enable us to improve the result of the naive Bayes classifier in a satisfactory way.
机译：已知朴素的贝叶斯分类器可以通过简单的过程获得良好的结果。该方法基于给定要分类的变量的属性变量的独立性。在没有验证该假设的真实数据库中，该分类器继续提供良好的结果。为了提高该方法的准确性，已经进行了各种工作，以尝试重建属性的集合并将它们结合在一起，以使新集合之间具有独立性，尽管每个集合中的元素是相互依赖的。这些方法包含在称为半朴素贝叶斯分类器的方法中。在本文中，我们提出了不确定性度量在概率分布的封闭集和凸集（也称为分集）上的应用。我们通过不精确的Dirichlet模型以一组概率间隔（一个crecre集）表示从数据库中获得的信息，并且在crecre集上使用不确定性度量来重构属性集（例如所提到的那些属性），这将使我们能够以令人满意的方式改进朴素贝叶斯分类器的结果。

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