{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/classification-with-costly-features-using","title":"Classification with Costly Features using Deep Reinforcement Learning","arxiv_id":"1711.07364","date":"2017-11-20","proceeding":null,"authors":["Jaromír Janisch","Tomáš Pevný","Viliam Lisý"],"abstract":"We study a classification problem where each feature can be acquired for a\ncost and the goal is to optimize a trade-off between the expected\nclassification error and the feature cost. We revisit a former approach that\nhas framed the problem as a sequential decision-making problem and solved it by\nQ-learning with a linear approximation, where individual actions are either\nrequests for feature values or terminate the episode by providing a\nclassification decision. On a set of eight problems, we demonstrate that by\nreplacing the linear approximation with neural networks the approach becomes\ncomparable to the state-of-the-art algorithms developed specifically for this\nproblem. The approach is flexible, as it can be improved with any new\nreinforcement learning enhancement, it allows inclusion of pre-trained\nhigh-performance classifier, and unlike prior art, its performance is robust\nacross all evaluated datasets.","url_abs":"http://arxiv.org/abs/1711.07364v2","url_pdf":"http://arxiv.org/pdf/1711.07364v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"classification-with-costly-features-using","repo_url":"https://github.com/jaromiru/cwcf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":null,"task_name":"Classification with Costly Features"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"q-learning","task_name":"Q-Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"sequential-decision-making","task_name":"Sequential Decision Making"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.07364","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}