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A dynamic attribute reduction algorithm based on relative neighborhood discernibility degree.

Weibing Feng1, Tiantian Sun2

  • 1College of Science, Xi'an University of Science and Technology, Xi'an, China.

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|July 8, 2024
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Summary
This summary is machine-generated.

This study introduces new attribute reduction algorithms for incomplete hybrid decision-making systems. The proposed dynamic algorithm significantly improves efficiency and classification accuracy, especially with unlabeled data.

Keywords:
Attribute reductionIncremental update mechanismRelative neighborhood discernibility degreeWeakly labeled data

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Area of Science:

  • Data Mining
  • Machine Learning
  • Decision Support Systems

Background:

  • Existing attribute reduction algorithms struggle with incomplete hybrid decision-making systems, showing low efficiency and accuracy.
  • Current methods often fail to adequately address unlabeled data types.

Purpose of the Study:

  • To develop novel attribute reduction algorithms for incomplete hybrid decision-making systems.
  • To enhance attribute reduction efficiency and classification accuracy, particularly for systems with unlabeled data.

Main Methods:

  • Redefinition of the weakly labeled relative neighborhood discernibility degree.
  • Development of a non-dynamic attribute reduction algorithm.
  • Proposal of an incremental update mechanism for a dynamic attribute reduction algorithm.

Main Results:

  • The proposed dynamic attribute reduction algorithm demonstrated superior attribute reduction efficiency compared to existing methods.
  • The dynamic algorithm achieved higher classification accuracy across 8 UCI datasets.
  • The improved methods effectively handle unlabeled data types.

Conclusions:

  • The novel dynamic attribute reduction algorithm offers significant improvements for incomplete hybrid decision-making systems.
  • The proposed methods provide a more effective approach to attribute reduction, enhancing both efficiency and accuracy.