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Related Concept Videos

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Related Experiment Videos

Self-training framework based on multi-granularity local cores for class-imbalanced semi-supervised classification in

Junnan Li1, Xiaosheng Su1, Leo Wang2

  • 1School of Artificial Intelligence and Big Data, Chongqing Industry Polytechnic University, Chongqing 401120, China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 6, 2026
PubMed
Summary

This study introduces a novel self-training framework, MGLC-CISSC, to improve performance in class-imbalanced semi-supervised learning. It effectively addresses labeled data insufficiency, particularly for minority classes, enhancing self-training methods in real-world applications.

Keywords:
Class-imbalanced classificationLocal coresMulti-granularity learningNatural neighborsSelf-training methodsSemi-supervised classification

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Self-training methods in semi-supervised learning struggle with limited labeled data, especially for minority classes.
  • Existing frameworks like K-means-SSC and LC-SSC show limitations in improving minority class performance and accuracy.
  • Class-imbalanced data exacerbates these issues, hindering the effectiveness of self-training.

Purpose of the Study:

  • To propose a novel self-training framework, MGLC-CISSC, to overcome limitations in class-imbalanced semi-supervised classification.
  • To enhance the improvement of insufficient labeled data, particularly for minority classes.
  • To increase the accuracy and reduce manual effort in labeling unlabeled data representatives.

Main Methods:

  • Introduced a multi-granularity local core search algorithm (MGLORE) to identify fine-grained unlabeled data in minority regions.
  • Developed a divide-and-conquer labeling strategy for class-imbalanced semi-supervised data (DCLSCISS) for accurate, low-effort labeling.
  • Integrated these components into a new self-training framework (MGLC-CISSC).

Main Results:

  • MGLC-CISSC significantly outperformed state-of-the-art solutions on class-imbalanced semi-supervised benchmark datasets.
  • The framework demonstrated superior performance in enhancing four self-training methods across two classifiers.
  • Effectively mitigated labeled-data insufficiency, with notable improvements for minority classes.

Conclusions:

  • The proposed MGLC-CISSC framework effectively addresses the challenges of class-imbalanced semi-supervised learning.
  • It significantly improves the performance of self-training methods by better handling insufficient labeled data, especially for minority classes.
  • MGLC-CISSC enhances the practicality of self-training in real-world, class-imbalanced semi-supervised scenarios.