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多种策略改进朴素贝叶斯分类器 被引量：11

Improving Naive Bayes Classification Model in Various Methods
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摘要 朴素贝叶斯分类器是一种简单而高效的分类器,但是它的属性独立性假设使其无法表示现实世界属性之间的依赖关系,影响了它的分类性能。通过广泛深入的研究,对改进朴素贝叶斯分类器的多种策略进行了系统的分析和归类整理,为进一步的研究打下坚实的基础。 Naive Bayes classifier is a simple and effective classification method, but its attribute independence assumption makes it unable to express the dependence among attributes in the real world, and affects its classification performance. On the basis of extensive and deep research, various improving methods based on Naive Bayes classifier are analyzed and sorted out systematically, which provides the foundation for further research.
作者 张璠
机构地区 北京交通大学计算机与信息技术学院
出处 《微机发展》 2005年第4期35-36,39,共3页 Microcomputer Development
关键词 朴素贝叶斯 贝叶斯网络分类器 树扩张型贝叶斯 Naive Bayes Bayesian network classifiers tree-augmented Naive Bayes
分类号 TP301 [自动化与计算机技术—计算机系统结构]
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参考文献12

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共引文献43

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