Research about transferred feature selection based on GMDH
基于GMDH的迁移特征选择模型研究*

This paper proposed a transferred feature selection model based on group method of data handling (GMDH-TFS) by integrating transfer learning and the group method of data handling algorithm. Comparison on four data sets of UCI among GMDH-TFS, classification with full features(FULL), supervised forward feature selection (SFFS), forward semi-supervised feature selection(FW-SemiFS) and transferred feature selection(TFS) show that GMDH-TFS has a better performance than other methods as well as in the case of learning with small samples. GMDH-TFS can do feature selection when the data are under different distribution, and can get satisfactory results even the data are not enough.