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

arXiv: https://arxiv.org/abs/2404.12511 · HTML: https://mlsanity.com/paper/2404.12511/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 18 Apr 2024.

## Builds on (most cited first, 10 of 10)

- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [Cross-Entropy Loss Functions: Theoretical Analysis and Applications](https://mlsanity.com/paper/2304.07288/) (2023)
- [In-context Autoencoder for Context Compression in a Large Language Model](https://mlsanity.com/paper/2307.06945/) (2023)
- [Microsoft Malware Classification Challenge](https://mlsanity.com/paper/1802.10135/) (2018)
- [Fast Rates for Maximum Entropy Exploration](https://mlsanity.com/paper/2303.08059/) (2023)
- [Information Flow in Deep Neural Networks](https://mlsanity.com/paper/2202.06749/) (2022)
- [Explainability through uncertainty: Trustworthy decision-making with neural networks](https://mlsanity.com/paper/2403.10168/) (2024)
- [Understanding Entropic Regularization in GANs](https://mlsanity.com/paper/2111.01387/) (2021)
- [An Entropy-Based Model for Hierarchical Learning](https://mlsanity.com/paper/2212.14681/) (2022)
- [Subsethood Measures of Spatial Granules](https://mlsanity.com/paper/2309.02662/) (2023)

## Built on by (most cited first, 1 of 1)

- [Bayesian-Guided Diversity in Sequential Sampling for Recommender Systems](https://mlsanity.com/paper/2506.21617/) (2025)

## Abstract (arXiv)

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 with the uncertainty quantification of Shannon entropy, applied across a spectrum of machine learning algorithms. Our methodology is rigorously tested on various datasets, showcasing its capability to not only assess predictive performance but also to illuminate the underlying data complexity and model robustness. The results underscore the utility of this integrated approach in enhancing the evaluation landscape of machine learning, offering a multi-faceted perspective that balances accuracy with a profound understanding of data attributes and model dynamics. This paper contributes a groundbreaking perspective to machine learning evaluation, proposing a method that encapsulates a holistic view of model performance, thereby facilitating more informed decision-making in model selection and application.