{"type":"ScholarlyArticle","title":"Feature and Instance Joint Selection: A Reinforcement Learning Perspective","authors":[{"name":"Wei Fan","slug":"wei-fan","given":"Wei","family":"Fan","affiliation":"University of Central Florida"},{"name":"Kunpeng Liu","slug":"kunpeng-liu","given":"Kunpeng","family":"Liu","affiliation":"Portland State University"},{"name":"Hao Liu","slug":"hao-liu","given":"Hao","family":"Liu","affiliation":"Hong Kong University of Science and Technology"},{"name":"Hengshu Zhu","slug":"hengshu-zhu","given":"Hengshu","family":"Zhu","affiliation":"Baidu Talent Intelligence Center"},{"name":"Hui Xiong","slug":"hui-xiong","given":"Hui","family":"Xiong","affiliation":"Rutgers University"},{"name":"Yanjie Fu","slug":"yanjie-fu","given":"Yanjie","family":"Fu","affiliation":"University of Central Florida"}],"abstract":"Feature selection and instance selection are two important techniques of data processing. However, such selections have mostly been studied separately, while existing work towards the joint selection conducts feature/instance selection coarsely; thus neglecting the latent fine-grained interaction between feature space and instance space. To address this challenge, we propose a reinforcement learning solution to accomplish the joint selection task and simultaneously capture the interaction between the selection of each feature and each instance. In particular, a sequential-scanning mechanism is designed as action strategy of agents and a collaborative-changing environment is used to enhance agent collaboration. In addition, an interactive paradigm introduces prior selection knowledge to help agents for more efficient exploration. Finally, extensive experiments on real-world datasets have demonstrated improved performances.","doi":"10.24963/ijcai.2022/280","url":"https://ijcai.org/proceedings/2022/280","pdf":"https://ijcai.org/proceedings/2022/0280.pdf","markdown":"https://ijcai.org/proceedings/2022/280.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/280","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Data Mining","track":"Main Track","number":280,"pages":{"first":2016,"last":2022},"keywords":["Machine Learning: Applications","Data Mining: Applications"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986566","length_sec":87,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984709","length_sec":348,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Aggarwal and Yu, 2001] Charu C Aggarwal and Philip S Yu. 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