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.

该研究论文提出了一种新颖的框架，用于在数据预处理中进行持续特征选择，特别是在存在未知类别可能出现的开放和动态环境下。研究方法结合了持续学习和颗粒球计算的优势，构建了颗粒球知识库来检测未知类别，并促进先前学到的知识的进一步特征选择。通过对公共基准数据集的广泛实验结果表明，该方法在效果和效率方面优于现有的特征选择方法。