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连续属性一阶贝叶斯衍生分类器学习与集成*

DOI: 10.16451/j.cnki.issn1003-6059.201506003, PP. 499-506

Keywords: 贝叶斯衍生分类器,高斯copula函数,分类器集成,贝叶斯网络,连续属性

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Abstract:

依据copula和贝叶斯网络理论,将高斯copula函数、引入平滑参数的高斯核函数和以分类器的分类准确性为标准的属性父结点贪婪选择等相结合,综合考虑效率和可靠性,进行连续属性一阶贝叶斯衍生分类器学习、优化和集成.使用UCI数据库中连续属性分类数据进行实验,结果显示,经过优化和集成的一阶连续属性贝叶斯衍生分类器具有良好的分类准确性.

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