The paper introduces a novel framework for continual feature selection (CFS) in data preprocessing, focusing on an open and dynamic environment. The CFS method combines continual learning with granular-ball computing to detect unknown classes and facilitate knowledge transfer. It consists of two stages: initial learning and open learning. The proposed method aims to establish an initial knowledge base through multi-granularity representation using granular-balls and then utilize prior knowledge to identify unknowns, update the knowledge base, reinforce old knowledge, and integrate new knowledge. Experimental results demonstrate the superiority of the method compared to state-of-the-art feature selection methods.
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by Xuemei Cao,X... at arxiv.org 03-18-2024
https://arxiv.org/pdf/2403.10253.pdfDeeper Inquiries