{"type":"ScholarlyArticle","title":"TFWT: Tabular Feature Weighting with Transformer","authors":[{"name":"Xinhao Zhang","slug":"xinhao-zhang","given":"Xinhao","family":"Zhang","affiliation":"Portland State University"},{"name":"Zaitian Wang","slug":"zaitian-wang","given":"Zaitian","family":"Wang","affiliation":"Computer Network Information Center , Chinese Academy of Sciences; University of Chinese Academy of Sciences , Chinese Academy of Sciences"},{"name":"Lu Jiang","slug":"lu-jiang","given":"Lu","family":"Jiang","affiliation":"Dalian Maritime University"},{"name":"Wanfu Gao","slug":"wanfu-gao","given":"Wanfu","family":"Gao","affiliation":"Jilin University"},{"name":"Pengfei Wang","slug":"pengfei-wang","given":"Pengfei","family":"Wang","affiliation":"Computer Network Information Center , Chinese Academy of Sciences; University of Chinese Academy of Sciences , Chinese Academy of Sciences"},{"name":"Kunpeng Liu","slug":"kunpeng-liu","given":"Kunpeng","family":"Liu","affiliation":"Portland State University"}],"abstract":"In this paper, we propose a novel feature weighting method to address the limitation of existing feature processing methods for tabular data. Typically the existing methods assume equal importance across all samples and features in one dataset. This simplified processing methods overlook the unique contributions of each feature, and thus may miss important feature information. As a result, it leads to suboptimal performance in complex datasets with rich features. To address this problem, we introduce Tabular Feature Weighting with Transformer, a novel feature weighting approach for tabular data. Our method adopts Transformer to capture complex feature dependencies and contextually assign appropriate weights to discrete and continuous features. Besides, we employ a reinforcement learning strategy to further fine-tune the weighting process. Our extensive experimental results across various real-world datasets and diverse downstream tasks show the effectiveness of TFWT and highlight the potential for enhancing feature weighting in tabular data analysis.","doi":"10.24963/ijcai.2024/284","url":"https://ijcai.org/proceedings/2024/284","pdf":"https://ijcai.org/proceedings/2024/0284.pdf","markdown":"https://ijcai.org/proceedings/2024/284.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/284","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":284,"pages":{"first":2570,"last":2578},"keywords":["Data Mining: DM: Applications","Machine Learning: ML: Feature extraction, selection and dimensionality reduction"],"videos":[],"references":[{"position":1,"raw":"[Barbe and Bertail, 1995] Philippe Barbe and Patrice Bertail. 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