May 15, 2024, 4:42 a.m. | Xinhao Zhang, Zaitian Wang, Lu Jiang, Wanfu Gao, Pengfei Wang, Kunpeng Liu

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.08403v1 Announce Type: new
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 …

abstract arxiv cs.lg data dataset equal feature features importance novel paper processing samples simplified tabular tabular data transformer type unique

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

Seeking Developers and Engineers for AI T-Shirt Generator Project

@ Chevon Hicks | Remote

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Business Intelligence Analyst Insights & Reporting

@ Bertelsmann | Hilversum, NH, NL, 1217WP