基于分布和逆文本类别指数的特征迁移加权算法  

Feature transfer weighting algorithm based on distribution and term frequency-inverse class frequency

作　　者：邱云飞[1] 刘世兴[1] 林明明[1] 邵良杉[2] 

机构地区：[1]辽宁工程技术大学软件学院,辽宁葫芦岛125105 [2]辽宁工程技术大学系统工程研究所,辽宁葫芦岛125105

出　　处：《计算机应用》2015年第6期1643-1648,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(70971059);辽宁省创新团队项目(2009T045);辽宁省高等学校杰出青年学者成长计划项目(LJQ2012027)

摘　　要：传统机器学习面临一个难题,即当训练数据与测试数据不再服从相同分布时,由训练集得到的分类器无法对测试集文本准确分类。针对该问题,根据迁移学习原理,在源领域和目标领域的交集特征中,依据改进的特征分布相似度进行特征加权;在非交集特征中,引入语义近似度和新提出的逆文本类别指数(TF-ICF),对特征在源领域内进行加权计算,充分利用大量已标记的源领域数据和少量已标记的目标领域数据获得所需特征,以便快速构建分类器。在文本数据集20Newsgroups和非文本数据集UCI中的实验结果表明,基于分布和逆文本类别指数的特征迁移加权算法能够在保证精度的前提下对特征快速迁移并加权。Traditional machine learning faces a problem： when the training data and test data no longer obey the same distribution, the classifier trained by training data can＇t classify test data accurately. To solve this problem, according to the transfer learning principle, the features were weighted according to the improved distribution similarity of source domain and target domain＇s intersection features. The semantic similarity and Term Frequency-Inverse Class Frequency （TF-ICF） were used to weight non-intersection features in source domain. Lots of labeled source domain data and a little labeled target domain were used to obtain the required features for building text classifier quickly. The experimental results on test dataset 20Newsgroups and non-text dataset UCI show that feature transfer weighting algorithm based on distribution and TF-ICF can transfer and weight features rapidly while guaranteeing precision.

关 键 词：迁移学习 特征分布 逆文本类别指数 语义近似度 特征加权 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]