Classification with Costly Features Using Deep Reinforcement Learning

Authors

  • Jaromír Janisch Czech Technical University in Prague
  • Tomáš Pevný Czech Technical University in Prague
  • Viliam Lisý Czech Technical University in Prague

DOI:

https://doi.org/10.1609/aaai.v33i01.33013959

Abstract

We study a classification problem where each feature can be acquired for a cost and the goal is to optimize a trade-off between the expected classification error and the feature cost. We revisit a former approach that has framed the problem as a sequential decision-making problem and solved it by Q-learning with a linear approximation, where individual actions are either requests for feature values or terminate the episode by providing a classification decision. On a set of eight problems, we demonstrate that by replacing the linear approximation with neural networks the approach becomes comparable to the state-of-the-art algorithms developed specifically for this problem. The approach is flexible, as it can be improved with any new reinforcement learning enhancement, it allows inclusion of pre-trained high-performance classifier, and unlike prior art, its performance is robust across all evaluated datasets.

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Published

2019-07-17

How to Cite

Janisch, J., Pevný, T., & Lisý, V. (2019). Classification with Costly Features Using Deep Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 3959-3966. https://doi.org/10.1609/aaai.v33i01.33013959

Issue

Section

AAAI Technical Track: Machine Learning