Feature transfer weighting algorithm based on distribution and term frequency-inverse class frequency
QIU Yunfei1, LIU Shixing1, LIN Mingming1, SHAO Liangshan2
1. School of Software, Liaoning Technical University, Huludao Liaoning 125105, China;
2. System Engineering Institute, Liaoning Technical University, Huludao Liaoning 125105, China
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.
邱云飞, 刘世兴, 林明明, 邵良杉. 基于分布和逆文本类别指数的特征迁移加权算法[J]. 计算机应用, 2015, 35(6): 1643-1648.
QIU Yunfei, LIU Shixing, LIN Mingming, SHAO Liangshan. Feature transfer weighting algorithm based on distribution and term frequency-inverse class frequency. Journal of Computer Applications, 2015, 35(6): 1643-1648.
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2015, Vol. 35 
