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An efficient framework based on local multi-representatives and noise-robust synthetic example generation for

Junnan Li1, Shun Fu1, Wei Fu1

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

Neural Networks : the Official Journal of the International Neural Network Society
|January 31, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for semi-supervised classification, enhancing self-labeled methods by addressing class overlap issues. The new approach improves classifier accuracy and reduces manual effort in training data.

Keywords:
Classification paradigmsDivide-and-conquer self-labelingLocal multi-representativesOversampling techniqueSelf-labeled wrapping framework

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Area of Science:

  • Machine Learning
  • Computer Science
  • Artificial Intelligence

Background:

  • Self-labeled methods in semi-supervised classification leverage both labeled and unlabeled data but are limited by the quantity and distribution of labeled instances.
  • Existing sophisticated methods like SEG-SSC, k-means-SSC, LC-SSC, and LCSEG-SSC struggle with overlapping classes, leading to suboptimal labeled instance distribution and noisy synthetic data.
  • These limitations result in low accuracy and high manual intervention in predicting unlabeled representatives.

Purpose of the Study:

  • To propose a novel framework, local multi-representatives and noise-robust synthetic example generation (LMR-NRSEG-SSC), to overcome the restrictions of existing self-labeled methods.
  • To enhance the labeled instance distribution and number by effectively handling overlapping classes in semi-supervised classification.
  • To improve the overall accuracy and reduce manual interference in self-labeled classification tasks.

Main Methods:

  • A local multi-representatives search algorithm with multi-granularity is employed to partition data and identify multiple representatives within clusters.
  • A divide-and-conquer self-labeling strategy is utilized to predict unlabeled local multi-representatives, thereby refining the labeled instance distribution.
  • A noise-robust oversampling technique based on local multi-representatives generates high-quality, low-noise synthetic labeled instances to increase the labeled data pool.

Main Results:

  • The proposed LMR-NRSEG-SSC framework effectively addresses the limitations of previous methods in scenarios with overlapping classes.
  • Experiments show that LMR-NRSEG-SSC significantly improves the performance of two advanced self-labeled methods.
  • The framework outperformed seven other sophisticated self-labeled frameworks on extensive benchmark datasets.

Conclusions:

  • LMR-NRSEG-SSC offers a robust solution for semi-supervised classification, particularly in challenging datasets with overlapping classes.
  • The method enhances classifier training by improving both the distribution and number of labeled instances.
  • This framework provides a significant advancement in self-labeled classification, offering higher accuracy and reduced manual effort.