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

Aug 2, 2026

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This research paper presents a novel framework that generalizes machine learning evaluation by integrating Shannon entropy with rough set theory. Shannon entropy is used to quantify uncertainty, while rough set theory contributes granular analysis of vagueness and indiscernibility in data. By combining uncertainty quantification with granularity, the approach aims to reveal intrinsic data structure and improve interpretability of machine learning models. Experiments across multiple datasets assess predictive performance as well as model robustness and data complexity, supporting more informed model selection and application.

LanguageEnglish

CategoryResearch & Report

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Pages11

Reading Time28 min

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Introduction

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Generalizing Machine Learning Evaluation through the Integration of Shannon Entropy and Rough Set Theory Olga Cherednichenko1, Dmytro Chernyshov 2, Dmytro Sytnikov2 and Polina Sytnikova2 1 University of Lyon 2, 5 avenue Mendès, Lyon, 69676, France 2 National University of RadioElectronics, Nauky ave. 14, Kharkiv, 61166, Ukraine 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 synergizesthe 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. Keywords Machine learning, entropy, information theory, rough set theory, model evaluation 1. Introduction In the evolving landscape of machine learning, the quest for robust evaluation metrics that transcend mere predictive accuracy is paramount. This research delves into an innovative integration of two mathematical concepts: Shannon entropy[1] and rough set theory[2], to forge a novel pathway in machine learning evaluation. Shannon entropy, a cornerstone in information theory, quantifies the uncertainty or the informational content within a system. It has been extensively applied across various domains, offering insights into the unpredictability or the inherent informational richness of datasets. Rough set theory, on the other hand, provides a framework to deal with vagueness and indiscernibility in data, enabling the analysis of data's granularity and the discernment of patterns within an ambiguous informational landscape[3,4] . The intersection of these two theories presents a fertile ground for advancing machine learning evaluation. Traditional metrics, while effective in gauging model performance, often overlook the nuanced interplay of data features and their collective impact on the learning process. The integration of entropy and rough set theory proposes a more holistic approach, considering not just the outcome but the informational dynamics and structural intricacies of the data being processed. The primary objective of this research is to establish a methodological framework that employs this integration to offer a more nuanced and comprehensive evaluation of machine COLINS-2024: 8th International Conference on Computational Linguistics and Intelligent Systems, April 12–13, 2024, Lviv, Ukraine [olga.cherednichenko@univ-lyon2.fr](olga.cherednichenko@univ-lyon2.fr) (O. Cherednichenko); [dmytro.chernyshov@nure.ua](dmytro.chernyshov@nure.ua) (D. Chernyshov); [dmytro.sytnikov@nure.ua](dmytro.sytnikov@nure.ua) (D. Sytnikov); [polina.sytnikova@nure.ua](polina.sytnikova@nure.ua) (P. Sytnikova) 0000-0002-9391-5220 (O. Cherednichenko); 0009-0003-2773-7467 (D. Chernyshov); 0000-0003-1240-7900 (D. Sytnikov); 0000-0002-6688-4641 (P. Sytnikova) © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attr

FAQs

What problem does the integrated framework target in machine learning evaluation?

It targets limitations of evaluation metrics that focus mainly on predictive accuracy while missing the informational dynamics and structural intricacies of the data.

How do Shannon entropy and rough set theory complement each other?

Shannon entropy quantifies uncertainty and informational content, while rough set theory provides a granular way to handle vagueness and indiscernibility, enabling deeper insight into data structure.

What aspects of models and data are evaluated in the proposed methodology?

The methodology assesses predictive performance and also illuminates underlying data complexity and model robustness, aiming for a balance between accuracy and interpretability.

Aug 2, 2026

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