摘要
已知朴素贝叶斯分类器使用两步策略的分类方法提高了两类中文文本分类的效率,本文在此基础上,研究3个问题:1可以使用两步策略分类方法的分类器须满足的条件;23种理论上可用两步策略进行文本分类的分类器;3实验比较Rocchio、朴素贝叶斯、KNN 3种分类器两两组合后应用于多类英语文本分类的效果。实验结果表明:Rocchio、朴素贝叶斯、KNN 3种分类器满足两步策略分类的条件,且当KNN作第一步分类器,朴素贝叶斯作第二步分类器时分类效果最好。
Naive Bayesian classifier is known to use two-step classification strategy to improve the efficiency of two types of Chinese text categorization. This paper tries to solve the following three questions：（1） the condition of a classifier to be fulfilled by using two-step strategy text classification, （2） the theoretical analysis of the three classifiers which can be used for two-step strategy text classification, （3） experimental results comparison of Rocchio,Naive Bayes,KNN combination used in many types of English text classification. Experimental results show that the Rocchio,NB and KNN satisfy the condi- tions of two-step strategy. Best performance is achieved by using KNN as the first step classifier and NB as the second.
出处
《广西师范大学学报（自然科学版）》
CAS
北大核心
2011年第4期35-38,共4页
Journal of Guangxi Normal University:Natural Science Edition
基金
国家自然科学基金资助项目(60703010)
重庆市自然科学基金资助项目(2009BB2079)
