{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T03:19:45Z","timestamp":1774495185076,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,7,25]],"date-time":"2019-07-25T00:00:00Z","timestamp":1564012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1755946"],"award-info":[{"award-number":["1755946"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,7,25]]},"DOI":"10.1145\/3292500.3330868","type":"proceedings-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T13:17:26Z","timestamp":1564147046000},"page":"207-215","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":55,"title":["Automating Feature Subspace Exploration via Multi-Agent Reinforcement Learning"],"prefix":"10.1145","author":[{"given":"Kunpeng","family":"Liu","sequence":"first","affiliation":[{"name":"University of Central Florida, Orlando, FL, USA"}]},{"given":"Yanjie","family":"Fu","sequence":"additional","affiliation":[{"name":"University of Central Florida, Orlando, FL, USA"}]},{"given":"Pengfei","family":"Wang","sequence":"additional","affiliation":[{"name":"CNIC, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Le","family":"Wu","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, Hefei, China"}]},{"given":"Rui","family":"Bo","sequence":"additional","affiliation":[{"name":"Missouri Univ. of Sci. and Tech., Rolla, MO, USA"}]},{"given":"Xiaolin","family":"Li","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}]}],"member":"320","published-online":{"date-parts":[[2019,7,25]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000006"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2013.11.024"},{"key":"e_1_3_2_1_3_1","series-title":"Series B (methodological)","volume-title":"Maximum likelihood from incomplete data via the EM algorithm. Journal of the royal statistical society","author":"Dempster Arthur P","year":"1977","unstructured":"Arthur P Dempster , Nan M Laird , and Donald B Rubin . 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the royal statistical society . Series B (methodological) ( 1977 ), 1--38. Arthur P Dempster, Nan M Laird, and Donald B Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the royal statistical society. Series B (methodological) (1977), 1--38."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.camwa.2013.06.031"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944974"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/2503308.2503311"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2006.01.007"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/1764441.1764453"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1012487302797"},{"key":"e_1_3_2_1_10_1","unstructured":"Mark A Hall. 1999. Feature selection for discrete and numeric class machine learning. (1999).  Mark A Hall. 1999. Feature selection for discrete and numeric class machine learning. (1999)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/347090.347169"},{"key":"e_1_3_2_1_12_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 ( 2016 ). Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(97)00043-X"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2009.71"},{"key":"e_1_3_2_1_15_1","volume-title":"Genetic algorithms in feature selection. Genetic algorithms in molecular modeling","author":"Leardi Riccardo","unstructured":"Riccardo Leardi . 1996. Genetic algorithms in feature selection. Genetic algorithms in molecular modeling . Elsevier , 67--86. Riccardo Leardi. 1996. Genetic algorithms in feature selection. Genetic algorithms in molecular modeling. Elsevier, 67--86."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISGTEUROPE.2010.5638914"},{"key":"e_1_3_2_1_17_1","volume-title":"Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning. arXiv preprint arXiv:1802.06444","author":"Lin Kaixiang","year":"2018","unstructured":"Kaixiang Lin , Renyu Zhao , Zhe Xu , and Jiayu Zhou . 2018. Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning. arXiv preprint arXiv:1802.06444 ( 2018 ). Kaixiang Lin, Renyu Zhao, Zhe Xu, and Jiayu Zhou. 2018. Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning. arXiv preprint arXiv:1802.06444 (2018)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992699"},{"key":"e_1_3_2_1_19_1","volume-title":"Nature","volume":"518","author":"Mnih Volodymyr","year":"2015","unstructured":"Volodymyr Mnih , Koray Kavukcuoglu , David Silver , Andrei A Rusu , Joel Veness , Marc G Bellemare , Alex Graves , Martin Riedmiller , Andreas K Fidjeland , Georg Ostrovski , 2015 . Human-level control through deep reinforcement learning . Nature , Vol. 518 , 7540 (2015), 529. Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al. 2015. Human-level control through deep reinforcement learning. Nature, Vol. 518, 7540 (2015), 529."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TC.1977.1674939"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2005.159"},{"key":"e_1_3_2_1_22_1","volume-title":"Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games. arXiv preprint arXiv:1703.10069","author":"Peng Peng","year":"2017","unstructured":"Peng Peng , Ying Wen , Yaodong Yang , Quan Yuan , Zhenkun Tang , Haitao Long , and Jun Wang . 2017. Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games. arXiv preprint arXiv:1703.10069 ( 2017 ). Peng Peng, Ying Wen, Yaodong Yang, Quan Yuan, Zhenkun Tang, Haitao Long, and Jun Wang. 2017. Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games. arXiv preprint arXiv:1703.10069 (2017)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btm344"},{"key":"e_1_3_2_1_24_1","volume-title":"Prioritized experience replay. arXiv preprint arXiv:1511.05952","author":"Schaul Tom","year":"2015","unstructured":"Tom Schaul , John Quan , Ioannis Antonoglou , and David Silver . 2015. Prioritized experience replay. arXiv preprint arXiv:1511.05952 ( 2015 ). Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver. 2015. Prioritized experience replay. arXiv preprint arXiv:1511.05952 (2015)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/NEUREL.2016.7800108"},{"key":"e_1_3_2_1_26_1","volume-title":"Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing. Mechanical systems and signal processing","author":"Sugumaran V","year":"2007","unstructured":"V Sugumaran , V Muralidharan , and KI Ramachandran . 2007. Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing. Mechanical systems and signal processing , Vol. 21 , 2 ( 2007 ), 930--942. V Sugumaran, V Muralidharan, and KI Ramachandran. 2007. Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing. Mechanical systems and signal processing, Vol. 21, 2 (2007), 930--942."},{"key":"e_1_3_2_1_27_1","volume-title":"Reinforcement learning: An introduction","author":"Sutton Richard S","unstructured":"Richard S Sutton and Andrew G Barto . 2018. Reinforcement learning: An introduction . MIT press . Richard S Sutton and Andrew G Barto. 2018. Reinforcement learning: An introduction .MIT press."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0172395"},{"key":"e_1_3_2_1_29_1","series-title":"Series B (Methodological)","volume-title":"Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society","author":"Tibshirani Robert","year":"1996","unstructured":"Robert Tibshirani . 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society . Series B (Methodological) ( 1996 ), 267--288. Robert Tibshirani. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological) (1996), 267--288."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220096"},{"key":"e_1_3_2_1_31_1","volume-title":"Feature subset selection using a genetic algorithm. Feature extraction, construction and selection","author":"Yang Jihoon","unstructured":"Jihoon Yang and Vasant Honavar . 1998. Feature subset selection using a genetic algorithm. Feature extraction, construction and selection . Springer , 117--136. Jihoon Yang and Vasant Honavar. 1998. Feature subset selection using a genetic algorithm. Feature extraction, construction and selection. Springer, 117--136."},{"key":"e_1_3_2_1_32_1","volume-title":"Mean Field Multi-Agent Reinforcement Learning. arXiv preprint arXiv:1802.05438","author":"Yang Yaodong","year":"2018","unstructured":"Yaodong Yang , Rui Luo , Minne Li , Ming Zhou , Weinan Zhang , and Jun Wang . 2018. Mean Field Multi-Agent Reinforcement Learning. arXiv preprint arXiv:1802.05438 ( 2018 ). Yaodong Yang, Rui Luo, Minne Li, Ming Zhou, Weinan Zhang, and Jun Wang. 2018. Mean Field Multi-Agent Reinforcement Learning. arXiv preprint arXiv:1802.05438 (2018)."},{"key":"e_1_3_2_1_33_1","first-page":"412","article-title":"A comparative study on feature selection in text categorization","volume":"97","author":"Yang Yiming","year":"1997","unstructured":"Yiming Yang and Jan O Pedersen . 1997 . A comparative study on feature selection in text categorization . In Icml , Vol. 97. 412 -- 420 . Yiming Yang and Jan O Pedersen. 1997. A comparative study on feature selection in text categorization. In Icml, Vol. 97. 412--420.","journal-title":"Icml"},{"key":"e_1_3_2_1_34_1","volume-title":"Proceedings of the 20th international conference on machine learning (ICML-03)","author":"Yu Lei","year":"2003","unstructured":"Lei Yu and Huan Liu . 2003 . Feature selection for high-dimensional data: A fast correlation-based filter solution . In Proceedings of the 20th international conference on machine learning (ICML-03) . 856--863. Lei Yu and Huan Liu. 2003. Feature selection for high-dimensional data: A fast correlation-based filter solution. In Proceedings of the 20th international conference on machine learning (ICML-03). 856--863."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240374"}],"event":{"name":"KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Anchorage AK USA","acronym":"KDD '19","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330868","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330868","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330868","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:26:02Z","timestamp":1750206362000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330868"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,25]]},"references-count":35,"alternative-id":["10.1145\/3292500.3330868","10.1145\/3292500"],"URL":"https:\/\/doi.org\/10.1145\/3292500.3330868","relation":{},"subject":[],"published":{"date-parts":[[2019,7,25]]},"assertion":[{"value":"2019-07-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}