一种新型加权朴素贝叶斯分类算法  

A new Weighted Naive Bayesian Classification Algorithm

作　　者：张春英[1] 王晶[1] 

机构地区：[1]河北理工大学理学院,河北唐山063009

出　　处：《微计算机信息》2010年第30期222-223,192,共3页Control & Automation

摘　　要：朴素贝叶斯算法是一种简单而高效的分类算法。但是它的属性独立性假设一般在现实问题中很难满足,这在某种程度上影响了它的分类能力。加权朴素贝叶斯分类器,通过放松其基础假设,来增强贝叶斯分类器的分类效果。本文利用Rough Set可辨识矩阵,提出了基于属性频率的朴素贝叶斯分类算法,对不同的条件属性赋予不同的权值,有效地提高了朴素贝叶斯算法的分类性能。通过实验证明,此算法计算量小,计算更加简便,更有效。Naive Bayes algorithm is a simple and efficient classification algorithm,but its conditional independence assumption is not always true in real life which is affected to some extent.Weighted Naive Bayesian classifier relaxes the conditional independence assumptions to increase accuracy.Based on Identifiability matrix of Rough Set,a new weighted naive Bayes method based on attribute frequency is proposed.Different condition attributes are weighted differently,the Naive Bayesian classification algorithm performance is improved effectively.Experiments have proved that the calculation of this algorithm is easier and more effective.

关 键 词：数据挖掘 朴素贝叶斯 属性频率 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]