摘要
朴素贝叶斯分类是一种简单而高效的分类模型,然而条件独立性假设在现实中很少出现,致使其性能有所下降。通过引入关联规则,从两方面来改善朴素贝叶斯分类的性能。一方面,通过对关联规则的挖掘,发现条件属性之间的关联关系,并且利用这种关联关系弱化朴素贝叶斯的独立性假设;另一方面,通过关联规则的置信度,给朴素贝叶斯加权。
Naive Bayes classification is a kind of simple and effective classification model.However,the performance of this model may be poor due to the assumption on the condition independence.By introducing association rules,this classification model can be improved in two way.On the one hand,the associated relationship between condition attributes can be found out through association rules mining,in order to weaken the independent assumption.On the other hand,Naive Bayes is weighted by computing the confidence of association rules.
出处
《计算机系统应用》
2010年第11期106-109,共4页
Computer Systems & Applications
关键词
分类模型
朴素贝叶斯
数据挖掘
置信度
关联规则
classification model
naive bayes
data mining
confidence level
association rules
