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Bayesian Prediction Model Based on Attribute Weighting and Kernel Density Estimations

机译：基于属性加权和核密度估计的贝叶斯预测模型

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摘要

Although naive Bayes learner has been proven to show reasonable performance in machine learning, it often suffers from a few problems with handling real world data. First problem is conditional independence; the second problem is the usage of frequency estimator. Therefore, we have proposed methods to solve these two problems revolving around naive Bayes algorithms. By using an attribute weighting method, we have been able to handle conditional independence assumption issue, whereas, for the case of the frequency estimators, we have found a way to weaken the negative effects through our proposed smooth kernel method. In this paper, we have proposed a compact Bayes model, in which a smooth kernel augments weights on likelihood estimation. We have also chosen an attribute weighting method which employs mutual information metric to cooperate with the framework. Experiments have been conducted on UCI benchmark datasets and the accuracy of our proposed learner has been compared with that of standard naive Bayes. The experimental results have demonstrated the effectiveness and efficiency of our proposed learning algorithm.
机译：尽管天真的贝叶斯学习器已被证明在机器学习中表现出合理的性能，但它在处理现实世界数据时经常会遇到一些问题。第一个问题是条件独立性。第二个问题是频率估计器的使用。因此，我们提出了解决围绕朴素贝叶斯算法的这两个问题的方法。通过使用属性加权方法，我们已经能够处理条件独立性假设问题，而对于频率估计器，我们已经找到了一种通过我们提出的平滑核方法来减弱负面影响的方法。在本文中，我们提出了一个紧凑的贝叶斯模型，其中平滑核增加了似然估计的权重。我们还选择了一种属性加权方法，该方法采用互信息度量与框架协作。已经对UCI基准数据集进行了实验，并将我们建议的学习者的准确性与标准朴素贝叶斯的准确性进行了比较。实验结果证明了我们提出的学习算法的有效性和效率。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第19期|170324.1-170324.7|共7页
  • 作者

    Xiang Zhong-Liang; Yu Xiang-Ru; Kang Dae-Ki;

  • 作者单位

    Weifang Univ Sci & Technol, Shouguang 262700, Shandong, Peoples R China;

    Weifang Univ Sci & Technol, Shouguang 262700, Shandong, Peoples R China;

    Dongseo Univ, Dept Comp & Informat Engn, Busan 617716, South Korea;

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