北京交通大学学报2018，Vol.42Issue(2)：14-21,8.DOI:10.11860/j.issn.1673-0291.2018.02.003
一种局部属性加权朴素贝叶斯分类算法
A locally attribute weighted naive Bayes classifier
摘要
Abstract
Naive Bayes(NB) classifier has exhibited excellent performance on many problem domains due to its simplicity and efficiency.In reality the conditional independence assumption of Naive Bayes isn't always true.Attribute weighting is one of the most popular methods to alleviate this assumption's influence on classification results.However,traditional classification models ignore characteristics of each test instance,and the weight vector learned from the whole training set failed to reflect each attribute's contribution of distinguishing each test instance correctly.To this end,a data driven lazy learning locally attribute weighted naive Bayes model is proposed.The attribute weights for each test instance are learned from its neighborhoods,and learned weights are employed to build the locally weighted model by optimization method.Experimental results on benchmark datasets demonstrate that the proposed approach is more accurate than other classical classifiers.关键词
朴素贝叶斯/懒惰式/属性加权/局部加权Key words
Naive Bayes/lazy learning/attribute weighting/locally weighted learning分类
信息技术与安全科学引用本文复制引用
张伟,王志海,原继东,刘海洋..一种局部属性加权朴素贝叶斯分类算法[J].北京交通大学学报,2018,42(2):14-21,8.基金项目
国家自然科学基金(61771058,61672086,61702030) （61771058,61672086,61702030）
北京市自然科学基金(4182052) （4182052）
中央高校基本科研业务费专项资金(2017YJS036)National Natural Science Foundation of China (61771058,61672086,61702030) （2017YJS036）
Beijing Municipal Natural Science Foundation (4182052) （4182052）
Fundamental Research Funds for the Central Universities (2017YJS036) （2017YJS036）
