Generalizing Machine Learning Evaluation through the Integration of Shannon Entropy and Rough Set Theory

arXiv:2404.12511v1 Announce Type: new
Abstract: This research paper delves into the innovative integration of Shannon entropy and rough set theory, presenting a novel approach to generalize the evaluation approach in machine learning. The conventional application of entropy, primarily focused on information uncertainty, is extended through its combination with rough set theory to offer a deeper insight into data's intrinsic structure and the interpretability of machine learning models. We introduce a comprehensive framework that synergizes the granularity of rough set theory …

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