Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding

Hajin Shim; Sung Ju Hwang; Eunho Yang

NeurIPS 2018poster97 citations

Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding

Abstract

We consider the problem of active feature acquisition where the goal is to sequentially select the subset of features in order to achieve the maximum prediction performance in the most cost-effective way at test time. In this work, we formulate this active feature acquisition as a jointly learning problem of training both the classifier (environment) and the RL agent that decides either to

BibTeX

@inproceedings{NEURIPS2018_e5841df2,
 author = {Shim, Hajin and Hwang, Sung Ju and Yang, Eunho},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding},
 url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/e5841df2166dd424a57127423d276bbe-Paper.pdf},
 volume = {31},
 year = {2018}
}