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Open Continual Feature Selection via Granular-Ball Knowledge Transfer

Xuemei Cao, Xin Yang, Shuyin Xia, Guoyin Wang, Tianrui Li

TL;DR

The GBCFS method focuses on constructing a granular-ball knowledge base to detect unknown classes and facilitate the transfer of previously learned knowledge for further feature selection, and devise an optimal feature subset mechanism that incorporates minimal new features into the existing optimal subset, often yielding superior results during each period.

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.

Open Continual Feature Selection via Granular-Ball Knowledge Transfer

TL;DR

The GBCFS method focuses on constructing a granular-ball knowledge base to detect unknown classes and facilitate the transfer of previously learned knowledge for further feature selection, and devise an optimal feature subset mechanism that incorporates minimal new features into the existing optimal subset, often yielding superior results during each period.

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.
Paper Structure (21 sections, 14 equations, 8 figures, 6 tables)

This paper contains 21 sections, 14 equations, 8 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Base Model and Definition
  4. Granular-Ball Computing
  5. Granular-Ball Neighborhood Rough Sets and Feature Selection
  6. The Proposed Approach
  7. Motivation
  8. Problem Formulation
  9. Framework
  10. Class Identification
  11. Granular-Ball Updating
  12. Feature Subset Enhancement
  13. Algorithm Design
  14. Experiments
  15. Benchmark Datasets
  16. ...and 6 more sections

Figures (8)

  • Figure 1: There are three dynamic changes in the data, and the colors red, blue, and green represent one of them.
  • Figure 2: Granular-balls generation process
  • Figure 3: Motivation of CFS. After adding new class data, a new feature subset is needed to represent the decision boundary.
  • Figure 4: Framework of the Proposed Approach CFS.
  • Figure 5: Continual Feature Selection (CFS)
  • ...and 3 more figures

Theorems & Definitions (5)

  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Definition 5