一种结合反馈信息的贝叶斯分类增量学习方法  

Incremental learning method of Bayesian classification combined with feedback information

作　　者：许明英[1,2] 尉永清[3] 赵静[1,2] 

机构地区：[1]山东师范大学信息科学与工程学院,济南250014 [2]山东省分布式计算机软件新技术重点实验室,济南250014 [3]山东警察学院公共教学部,济南250014

出　　处：《计算机应用》2011年第9期2530-2533,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(60873247);山东省高新自主创新专项(2008ZZ28);山东省自然科学基金重点资助项目(ZR2009GZ007)

摘　　要：贝叶斯分类器形成初期,训练集不完备,生成的分类器性能不理想且不能动态跟踪用户需求。针对此缺陷,提出一种结合反馈信息的贝叶斯分类增量学习方法。为有效降低特征间的冗余性,提高反馈特征子集的代表能力,用一种基于遗传算法的改进特征选择方法选取反馈集中最优特征子集修正分类器。通过实验分析了算法的性能,结果证明该算法能明显优化分类效果,且整体稳定性较好。Owing to the insufficiency of the training sets, the performance of the initial classifier is not satisfactory and can not track the users＇ needs dynamically. Concerning the defect, an incremental learning method of Bayesian classifier combined with feedback information was proposed. To reduce the redundancy between features effectively and improve representative ability of feedback feature subset, an improved feature selection method based on Genetic Algorithm （GA） was used to choose the best features from feedback sets to amend classifier. The experimental results show that the algorithm optimizes classification significantly and has good overall stability.

关 键 词：反馈信息 遗传算法 特征选择 朴素贝叶斯 增量学习 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]