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区间直觉模糊集的截集
  • ISSN号：1671-8747
  • 期刊名称：海南师范大学学报(自然科学版)
  • 时间：0
  • 页码：249-273
  • 分类：TP311.13[自动化与计算机技术—计算机软件与理论;自动化与计算机技术—计算机科学与技术]
  • 作者机构：[1]漳州师范学院计算机科学与工程系,福建漳州363000
  • 相关基金：国家自然科学基金项目（10971186）; 福建省教育厅重点项目（JA10202）
  • 相关项目：覆盖的约简理论及其在中医方剂配伍规律研究中的应用
作者： 吴燕华|李克典|
关键词： 分类, 朴素贝叶斯原理, 属性值贡献率, classification, Bayesian principle, the contribution rate of attribute value
中文摘要：

朴素贝叶斯分类是一种简单高效的方法.但是当属性独立性假设不成立时,有可能导致待测样本类别判断错误;且当待测样本到各类别的概率相同时,无法判断该样本类别,从而影响了它的分类准确率.本文提出基于属性值贡献率的朴素贝叶斯改进算法,利用待测样本的各个属性值在各类别的总贡献率判别该样本的类别.在蘑菇数据实验结果表明,该算法能有效提高分类的准确率.

英文摘要：

The Naive Bayesian is a simple and efficient way of classification.When the assumption of attribute independence does not hold,it possibly leads to misjudgment in types of the will-be-tested samples.When the will-be-tested samples have the same probabilities in all categories,it is unable to judge the type of samples.Those affect the accuracy in data＇s classification.An improved algorithm of Bayesian based on contribution rate of attribute value is proposed in the paper,that is,the type of samples will be judged by the total contribution rate of all attribute value of will-be-tested samples in all categories.The result of mushroom data experiments show that the improved algorithm can effectively improve the accuracy of data classification.

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覆盖的约简理论及其在中医方剂配伍规律研究中的应用
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