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.
出处
《计算机应用》
CSCD
北大核心
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)
关键词
反馈信息
遗传算法
特征选择
朴素贝叶斯
增量学习
feedback information
Genetic Algorithm （GA）
feature selection
Naive Bayesian
incremental learning
