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Classification with costly features as a sequential decision-making problem
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DOI：10.1007/s10994-020-05874-8.png)
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摘要
En 中文
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.
Keyword:
Sequential classification
Costly features
Adaptive feature acquisition
Datum-Wise classification
Prediction on budget
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2.9
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2.7K
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3.4W
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