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Constructing three-way classifier with interval granulation neighborhood rough sets based on uncertainty invariance.

Yongqi Wang1, Taihua Xu1, Rong Huang1

  • 1School of Computer, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China.

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|April 17, 2026
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Summary

This study introduces interval granulation into three-way decision (3WD) to create a novel classifier. The new method, 3WD-IGNRS, improves decision efficiency and generalizability for uncertain continuous data, outperforming existing models.

Keywords:
DBSCANclassifierfuzziness lossinterval granulation neighborhood rough setsthree-way decision

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Area of Science:

  • Data Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Three-way decision with neighborhood rough sets (3WDNRS) effectively handles uncertainty in continuous data.
  • Limitations of 3WDNRS include reliance on individual granules and the need for predefined thresholds.

Purpose of the Study:

  • To address limitations of 3WDNRS by introducing interval granulation (IG).
  • To develop an effective three-way classifier that enhances decision efficiency and model generalizability.

Main Methods:

  • Proposed an interval granulation method based on DBSCAN.
  • Developed an interval granulation neighborhood rough sets (IGNRS) model integrating IG with quality indicators.
  • Constructed a three-way classifier (3WD-IGNRS) using the IGNRS model and minimum fuzzy loss.

Main Results:

  • Conducted comparative experiments on 12 benchmark datasets against state-of-the-art granular-ball and classical machine learning classifiers.
  • The proposed 3WD-IGNRS model demonstrated consistent performance improvements.
  • Achieved an average accuracy improvement of 4.94% over the best granular-ball classifier.

Conclusions:

  • The proposed interval granulation approach effectively overcomes the limitations of traditional 3WDNRS.
  • 3WD-IGNRS offers a robust and more generalizable solution for decision-making with uncertain continuous data.
  • The method shows significant potential for applications requiring efficient and accurate classification under uncertainty.