Open Continual Feature Selection via Granular-Ball Knowledge Transfer

arXiv:2403.10253v1 Announce Type: new
Abstract: This paper presents a novel framework for continual feature selection (CFS) in data preprocessing, particularly in the context of an open and dynamic environment where unknown classes may emerge. CFS encounters two primary challenges: the discovery of unknown knowledge and the transfer of known knowledge. To this end, the proposed CFS method combines the strengths of continual learning (CL) with granular-ball computing (GBC), which focuses on constructing a granular-ball knowledge base to detect unknown classes …

abstract arxiv challenges context continual cs.lg data data preprocessing discovery dynamic environment feature feature selection framework knowledge novel paper transfer type via