Version 1
: Received: 25 March 2024 / Approved: 25 March 2024 / Online: 26 March 2024 (13:05:00 CET)
How to cite:
Fan, Z.; Chen, J.; Cui, H.; Song, J.; Xu, T. Optimizing Attribute Reduction in Multi-granularity Data through a Hybrid Supervised-Unsupervised Model. Preprints2024, 2024031548. https://doi.org/10.20944/preprints202403.1548.v1
Fan, Z.; Chen, J.; Cui, H.; Song, J.; Xu, T. Optimizing Attribute Reduction in Multi-granularity Data through a Hybrid Supervised-Unsupervised Model. Preprints 2024, 2024031548. https://doi.org/10.20944/preprints202403.1548.v1
Fan, Z.; Chen, J.; Cui, H.; Song, J.; Xu, T. Optimizing Attribute Reduction in Multi-granularity Data through a Hybrid Supervised-Unsupervised Model. Preprints2024, 2024031548. https://doi.org/10.20944/preprints202403.1548.v1
APA Style
Fan, Z., Chen, J., Cui, H., Song, J., & Xu, T. (2024). Optimizing Attribute Reduction in Multi-granularity Data through a Hybrid Supervised-Unsupervised Model. Preprints. https://doi.org/10.20944/preprints202403.1548.v1
Chicago/Turabian Style
Fan, Z., Jingjing Song and Taihua Xu. 2024 "Optimizing Attribute Reduction in Multi-granularity Data through a Hybrid Supervised-Unsupervised Model" Preprints. https://doi.org/10.20944/preprints202403.1548.v1
Abstract
Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have many shortcomings, such as how to deal with complex constraints and how to perform multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This model can select the optimal granularity level or attribute set according to the requirements. Finally, the proposed method is experimented with on 15 UCI datasets against six other attribute reduction algorithms. The experimental outcomes, upon analysis and comparative review, substantiate the reliability and consistency of the attribute reduction approach introduced in this study.
Keywords
rough sets; attribute reduction; multi-granularity; information gain
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
