Name
Abstract | ||
|---|---|---|
This work focuses on a specific classification problem, where the information about a sample is not readily available, but has to be acquired for a cost, and there is a per-sample budget. Inspired by real-world use-cases, we analyze average and hard variations of a directly specified budget. We postulate the problem in its explicit formulation and then convert it into an equivalent MDP, that can be solved with deep reinforcement learning. Also, we evaluate a real-world inspired setting with sparse training datasets with missing features. The presented method performs robustly well in all settings across several distinct datasets, outperforming other prior-art algorithms. The method is flexible, as showcased with all mentioned modifications and can be improved with any domain independent advancement in RL. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s10994-020-05874-8 | MACHINE LEARNING |
Keywords | DocType | Volume |
Sequential classification,Costly features,Adaptive feature acquisition,Datum-Wise classification,Prediction on budget | Journal | 109.0 |
Issue | ISSN | Citations |
8 | 0885-6125 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaromír Janisch | 1 | 2 | 0.70 |
| Tomáš Pevný | 2 | 1043 | 45.20 |
| Viliam Lisý | 3 | 219 | 26.66 |