Open Continual Feature Selection via Granular-Ball Knowledge Transfer
Xuemei Cao Xin Yang Shuyin Xia Guoyin Wang Tianrui Li
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 and
facilitate the transfer of previously learned knowledge for further feature
selection. CFS consists of two stages: initial learning and open learning. The
former aims to establish an initial knowledge base through multi-granularity
representation using granular-balls. The latter utilizes prior granular-ball
knowledge to identify unknowns, updates the knowledge base for granular-ball
knowledge transfer, reinforces old knowledge, and integrates new knowledge.
Subsequently, we devise an optimal feature subset mechanism that incorporates
minimal new features into the existing optimal subset, often yielding superior
results during each period. Extensive experimental results on public benchmark
datasets demonstrate our method's superiority in terms of both effectiveness
and efficiency compared to state-of-the-art feature selection methods.