{"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/feature-selection-as-monte-carlo-search-in","title":"Feature selection as Monte-Carlo Search in Growing Single Rooted Directed Acyclic Graph by Best Leaf Identification","arxiv_id":"1811.07531","date":"2018-11-19","proceeding":null,"authors":["Aurelien Pelissier","Atsuyoshi Nakamura","Koji Tabata"],"abstract":"Monte Carlo tree search (MCTS) has received considerable interest due to its\nspectacular success in the difficult problem of computer Go and also proved\nbeneficial in a range of other domains. A major issue that has received little\nattention in the MCTS literature is the fact that, in most games, different\nactions can lead to the same state, that may lead to a high degree of\nredundancy in tree representation and unnecessary additional computational\ncost. We extend MCTS to single rooted directed acyclic graph (SR-DAG), and\nconsider the Best Arm Identification (BAI) and the Best Leaf Identification\n(BLI) problem of an expanding SR-DAG of arbitrary depth. We propose algorithms\nthat are (epsilon, delta)-correct in the fixed confidence setting, and prove an\nasymptotic upper bounds of sample complexity for our BAI algorithm. As a major\napplication for our BLI algorithm, a novel approach for Feature Selection is\nproposed by representing the feature set space as a SR-DAG and repeatedly\nevaluating feature subsets until a candidate for the best leaf is returned, a\nproof of concept is shown on benchmark data sets.","url_abs":"http://arxiv.org/abs/1811.07531v2","url_pdf":"http://arxiv.org/pdf/1811.07531v2.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":"feature-selection-as-monte-carlo-search-in","repo_url":"https://github.com/Aurelien-Pelissier/Feature-Selection-as-Reinforcement-Learning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}