A Novel Method for Improving Deep Learning Performance Using Rough Set Theory

Abstract

Dimensionality reduction is a key stage in data processing. Eliminating irrelevant attributes from the input dataset enables more efficient processing and supports the development of robust Machine Learning models. In particular, when dealing with large datasets, attribute reduction algorithms can significantly reduce computational complexity while minimizing the loss of valuable information contained in the data. This paper explores the potential application of a Feature selection method based on Rough Set Theory for multivariate data. The experiments were conducted on the publicly available Taiwan Credit dataset. The results show that limiting the input feature space to a minimal set of attributes generated by a reduct selection algorithm not only reduces training and inference time for Machine Learning or Deep Learning models but may also improve their classification performance. The models achieved higher Accuracy and better adaptability to the input data, as confirmed by increased or comparable Precision and Recall scores. Additionally, the findings indicate that applying Feature selection using a reduct set can lead to more efficient data processing. These results suggest that integrating Rough Set Theory with Deep Learning models still represents a promising approach to analyzing attribute-value data.