Open Continual Feature Selection via Granular-Ball Knowledge Transfer

TL;DR

This paper introduces an open continual feature selection framework that leverages granular-ball computing to detect unknown classes and transfer knowledge, improving feature selection in dynamic environments.

Contribution

It combines continual learning with granular-ball computing to handle unknown class discovery and knowledge transfer in feature selection.

Findings

Outperforms state-of-the-art methods in effectiveness.

Demonstrates superior efficiency on benchmark datasets.

Effectively detects unknown classes and updates knowledge base.

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 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.