2022年 02期

摘要(Abstract)：

为了提高朴素贝叶斯算法的复合语言文本分类准确度和效率，将加权朴素贝叶斯算法用于复合语言文本分类，采用量子遗传算法对权重参数进行优化；根据贝叶斯定理建立语言文本分类模型，考查样本属性之间的差异对分类结果的影响；然后引入属性权重，形成加权朴素贝叶斯文本分类模型；利用遗传算法对权重参数进行优化，借助量子比特运算提高遗传优化效率，最终得到稳定的复合语言文本分类模型。结果表明，通过合理设置权重个数，量子遗传算法改善了加权朴素贝叶斯算法的文本分类性能，与常用语言文本分类算法对比，该算法具有较高的分类精度和分类效率，在复合语言文本分类中的适用性好。

关键词(KeyWords)： 量子遗传算法;加权朴素贝叶斯算法;复合语言文本;分类;量子比特

基金项目(Foundation): 国家自然科学基金项目(61662028);; 江西省科技厅科技计划项目(GJJ170447)

作者(Author): 隆峻,神显豪,丁小军,郭先春

DOI: 10.13349/j.cnki.jdxbn.20211011.001

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