# Open Continual Feature Selection via Granular-Ball Knowledge Transfer

arXiv: https://arxiv.org/abs/2403.10253 · HTML: https://mlsanity.com/paper/2403.10253/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 15 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [Task-Free Continual Learning](https://mlsanity.com/paper/1812.03596/) (2018)
- [A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning](https://mlsanity.com/paper/2009.01797/) (2020)
- [Open-environment Machine Learning](https://mlsanity.com/paper/2206.00423/) (2022)
- [An Efficient and Adaptive Granular-ball Generation Method in Classification Problem](https://mlsanity.com/paper/2201.04343/) (2022)
- [Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees](https://mlsanity.com/paper/1605.09131/) (2016)
- [Infinite Feature Selection: A Graph-based Feature Filtering Approach](https://mlsanity.com/paper/2006.08184/) (2020)
- [Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method](https://mlsanity.com/paper/2304.11171/) (2023)
- [An Efficient and Accurate Rough Set for Feature Selection, Classification and Knowledge Representation](https://mlsanity.com/paper/2201.00436/) (2021)

## Built on by (most cited first, 4 of 4)

- [ErrorEraser: Unlearning Data Bias for Improved Continual Learning](https://mlsanity.com/paper/2506.09347/) (2025)
- [Multi-Granularity Open Intent Classification via Adaptive Granular-Ball Decision Boundary](https://mlsanity.com/paper/2412.13542/) (2024)
- [Approximate Borderline Sampling using Granular-Ball for Classification Tasks](https://mlsanity.com/paper/2506.02366/) (2025)
- [Implicit Non-Causal Factors are Out via Dataset Splitting for Domain Generalization Object Detection](https://mlsanity.com/paper/2601.19127/) (2026)

## Abstract (arXiv)

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.