Within the field of supervised classification, the naïve Bayes (NB) classifier is a very simple and fast classification method that obtains good results, being even comparable with much more complex models. It has been proved that the NB model is strongly dependent on the estimation of conditional probabilities. In the literature, it had been shown that the classical and Laplace estimations of probabilities have some drawbacks and it was proposed a NB model that takes into account the a priori probabilities in order to estimate the conditional probabilities, which was called m-probability-estimation. With a very scarce experimentation, this approximation based on m-probability-estimation demonstrated to provide better results than NB with classical and Laplace estimations of probabilities. In this research, a new naïve Bayes variation is proposed, which is based on the m-probability-estimation version and takes into account imprecise probabilities in order to calculate the a priori probabilities. An exhaustive experimental research is carried out, with a large number of data sets and different levels of class noise. From this experimentation, we can conclude that the proposed NB model and the m-probability-estimation approach provide better results than NB with classical and Laplace estimation of probabilities. It will be also shown that the proposed NB implies an improvement over the m-probability-estimation model, especially when there is some class noise.

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关于在朴素贝叶斯分类器中使用 m 概率估计和不精确概率

在监督分类领域，朴素贝叶斯（NB）分类器是一种非常简单快速的分类方法，取得了很好的效果，甚至可以与更复杂的模型相媲美。已经证明，NB模型强烈依赖于条件概率的估计。在文献中，已经表明经典的概率估计和拉普拉斯估计存在一些缺点，因此提出了一种考虑先验概率的NB模型来估计条件概率，称为m-probability-estimate . 通过非常稀少的实验，这种基于 m 概率估计的近似证明比具有经典和拉普拉斯概率估计的 NB 提供更好的结果。在这项研究中，提出了一种新的朴素贝叶斯变体，它基于 m 概率估计版本，并考虑了不精确的概率以计算先验概率。进行了详尽的实验研究，具有大量数据集和不同级别的类噪声。从这个实验中，我们可以得出结论，所提出的 NB 模型和 m 概率估计方法提供了比 NB 的经典概率估计和拉普拉斯估计更好的结果。还将表明，所提出的 NB 意味着对 m 概率估计模型的改进，尤其是当存在一些类别噪声时。具有大量数据集和不同级别的类噪声。从这个实验中，我们可以得出结论，所提出的 NB 模型和 m 概率估计方法提供了比 NB 的经典概率估计和拉普拉斯估计更好的结果。还将表明，所提出的 NB 意味着对 m 概率估计模型的改进，尤其是当存在一些类别噪声时。具有大量数据集和不同级别的类噪声。从这个实验中，我们可以得出结论，所提出的 NB 模型和 m 概率估计方法提供了比 NB 的经典概率估计和拉普拉斯估计更好的结果。还将表明，所提出的 NB 意味着对 m 概率估计模型的改进，尤其是当存在一些类别噪声时。