Feb. 15, 2024, 5:41 a.m. | Xubin Wang, Haojiong Shangguan, Fengyi Huang, Shangrui Wu, Weijia Jia

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.08982v1 Announce Type: new
Abstract: Feature selection is a crucial step in data mining to enhance model performance by reducing data dimensionality. However, the increasing dimensionality of collected data exacerbates the challenge known as the "curse of dimensionality", where computation grows exponentially with the number of dimensions. To tackle this issue, evolutionary computational (EC) approaches have gained popularity due to their simplicity and applicability. Unfortunately, the diverse designs of EC methods result in varying abilities to handle different data, often …

arxiv cs.ai cs.lg cs.ne feature feature selection type

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