Feature Selection based on Variable Universe Rough Set

The variable universe model is an effective tool for handling uncertain knowledge. However, there remains room for improvement in the area of feature selection within this model. Addressing this gap, we propose a lower approximation reduction based on the variable universe model to enhance feature selection tasks. We design and implement corresponding algorithms, demonstrating through experimentation that our approach outperforms traditional method. Specifically, our method not only achieves rapid and effective feature selection but also significantly reduces data dimensionality. Leveraging the unique characteristics of the variable universe model, our approach accurately identifies key features within datasets, thereby enhancing efficiency and accuracy in data analysis.