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Variable granularity rough sets for semi-supervised feature selection
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DOI：10.1007/s13042-026-03353-5.png)
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Abstract
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Granular computing theory offers a well-established framework for measuring data uncertainty. However, in practical applications involving multi-density data, disparities in data distribution significantly affect uncertainty measurements, often causing conventional neighborhood methods to yield substantial errors. To address this issue, this study proposes a novel uncertainty measurement and data reduction framework tailored for multi-density data from the perspective of variable granularity, establishing the theoretical foundation for Variable Granularity Rough Sets (VGRSs). For samples with high classification uncertainty, the VGRS model assigns an initial granule to each sample, identifies internal class-label conflicts, and dynamically adjusts the granule. Whether a sample belongs to the lower approximation of a decision class is then determined by its adjusted neighborhood granule. Building upon the VGRS model, feature selection algorithms are further developed. Comprehensive numerical experiments demonstrate the superior performance of VGRS-based algorithms, validating the feasibility, effectiveness, and generalization capability of the proposed model.
Keywords:
Variable granularity
Rough sets
Multi-density data
Robust classification
Semi-supervised feature selection
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