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Causal Multi-Label Feature Selection in Federated Setting
March 12, 2024, 4:42 a.m. | Yukun Song, Dayuan Cao, Jiali Miao, Shuai Yang, Kui Yu
cs.LG updates on arXiv.org arxiv.org
Abstract: Multi-label feature selection serves as an effective mean for dealing with high-dimensional multi-label data. To achieve satisfactory performance, existing methods for multi-label feature selection often require the centralization of substantial data from multiple sources. However, in Federated setting, centralizing data from all sources and merging them into a single dataset is not feasible. To tackle this issue, in this paper, we study a challenging problem of causal multi-label feature selection in federated setting and propose …
abstract arxiv causal cs.lg data feature feature selection however mean merging multiple performance them type
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