Classification with costly features as a sequential decision-making problem.

Autor:	Janisch, Jaromír, Pevný, Tomáš, Lisý, Viliam
Předmět:	DEEP learning CLASSIFICATION REINFORCEMENT learning
Zdroj:	Machine Learning; Aug2020, Vol. 109 Issue 8, p1587-1615, 29p
Abstrakt:	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. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
Externí odkaz:	Zobrazit plný text záznamu Full text from SpringerLink