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Label propagation algorithm based on Roll-back detection and credibility assessment.

Ying Dong1, Wen Chen1, Hui Zhao1

  • 1College of Cyber Security, Sichuan University, Chengdu 610065, P. R. China.

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|April 3, 2020
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Summary
This summary is machine-generated.

The novel label propagation based on roll-back detection and credibility assessment (LPRC) algorithm improves classification accuracy. LPRC enhances label propagation by prioritizing certain samples and detecting errors, especially with limited labeled data.

Keywords:
Label propagationcertaintycredibility assessmentroll-back detectionsmall number of labeled samples

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Area of Science:

  • Machine Learning
  • Data Mining
  • Artificial Intelligence

Background:

  • Traditional label propagation algorithms (LPA) suffer from error expansion due to randomness and poor handling of uncertain data.
  • The efficiency of LPA is limited, particularly when the proportion of labeled samples is very small.

Purpose of the Study:

  • To introduce a new algorithm, label propagation based on roll-back detection and credibility assessment (LPRC), to enhance label propagation accuracy.
  • To address the limitations of traditional LPA in handling uncertain data and minimizing label error propagation.

Main Methods:

  • LPRC incorporates a credibility assessment of unlabeled samples before each propagation round, prioritizing more certain samples.
  • A roll-back detection mechanism is integrated into the iterative process to correct and improve label propagation accuracy.

Main Results:

  • LPRC demonstrates superior classification performance compared to 9 other algorithms on UCI datasets, especially with limited labeled samples (1-2%).
  • On synthetic datasets, LPRC achieved significant accuracy improvements: at least 26.31% in circles, and over 13.99% and 15.22% in moons and varied datasets, respectively, with only 1% labeled data.
  • For UCI datasets with 2% labeled data, LPRC showed average accuracy increases of 23.20% (wine), 20.82% (iris), 4.25% (australian), and 6.75% (breast).

Conclusions:

  • LPRC effectively improves label propagation accuracy and classification performance.
  • The proposed algorithm is particularly beneficial in scenarios with a scarcity of labeled data.
  • The accuracy of LPRC consistently increases with a higher number of labeled samples.