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

• URL: http://arxiv.org/abs/2404.12511v1
• Date: Thu, 18 Apr 2024 21:22:42 GMT
• Title: Generalizing Machine Learning Evaluation through the Integration of Shannon Entropy and Rough Set Theory
• Authors: Olga Cherednichenko, Dmytro Chernyshov, Dmytro Sytnikov, Polina Sytnikova,
• Abstract summary: We introduce a comprehensive framework that synergizes the granularity of rough set theory with the uncertainty quantification of Shannon entropy. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

Related papers

• On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning [85.75164588939185]
  We study the discriminative probabilistic modeling problem on a continuous domain for (multimodal) self-supervised representation learning. We conduct generalization error analysis to reveal the limitation of current InfoNCE-based contrastive loss for self-supervised representation learning.
  arXiv  Detail & Related papers  (2024-10-11T18:02:46Z)
• Dynamic Post-Hoc Neural Ensemblers [55.15643209328513]
  In this study, we explore employing neural networks as ensemble methods. Motivated by the risk of learning low-diversity ensembles, we propose regularizing the model by randomly dropping base model predictions. We demonstrate this approach lower bounds the diversity within the ensemble, reducing overfitting and improving generalization capabilities.
  arXiv  Detail & Related papers  (2024-10-06T15:25:39Z)
• Causal Inference Tools for a Better Evaluation of Machine Learning [0.0]
  We introduce key statistical methods such as Ordinary Least Squares (OLS) regression, Analysis of Variance (ANOVA) and logistic regression. The document serves as a guide for researchers and practitioners, detailing how these techniques can provide deeper insights into model behavior, performance, and fairness.
  arXiv  Detail & Related papers  (2024-10-02T10:03:29Z)
• Unified Explanations in Machine Learning Models: A Perturbation Approach [0.0]
  Inconsistencies between XAI and modeling techniques can have the undesirable effect of casting doubt upon the efficacy of these explainability approaches. We propose a systematic, perturbation-based analysis against a popular, model-agnostic method in XAI, SHapley Additive exPlanations (Shap) We devise algorithms to generate relative feature importance in settings of dynamic inference amongst a suite of popular machine learning and deep learning methods, and metrics that allow us to quantify how well explanations generated under the static case hold.
  arXiv  Detail & Related papers  (2024-05-30T16:04:35Z)
• Self-consistent Validation for Machine Learning Electronic Structure [81.54661501506185]
  Method integrates machine learning with self-consistent field methods to achieve both low validation cost and interpret-ability. This, in turn, enables exploration of the model's ability with active learning and instills confidence in its integration into real-world studies.
  arXiv  Detail & Related papers  (2024-02-15T18:41:35Z)
• A Bayesian Unification of Self-Supervised Clustering and Energy-Based Models [11.007541337967027]
  We perform a Bayesian analysis of state-of-the-art self-supervised learning objectives. We show that our objective function allows to outperform existing self-supervised learning strategies. We also demonstrate that GEDI can be integrated into a neuro-symbolic framework.
  arXiv  Detail & Related papers  (2023-12-30T04:46:16Z)
• GLUECons: A Generic Benchmark for Learning Under Constraints [102.78051169725455]
  In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. We model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints.
  arXiv  Detail & Related papers  (2023-02-16T16:45:36Z)
• Latent Variable Representation for Reinforcement Learning [131.03944557979725]
  It remains unclear theoretically and empirically how latent variable models may facilitate learning, planning, and exploration to improve the sample efficiency of model-based reinforcement learning. We provide a representation view of the latent variable models for state-action value functions, which allows both tractable variational learning algorithm and effective implementation of the optimism/pessimism principle. In particular, we propose a computationally efficient planning algorithm with UCB exploration by incorporating kernel embeddings of latent variable models.
  arXiv  Detail & Related papers  (2022-12-17T00:26:31Z)
• AcME -- Accelerated Model-agnostic Explanations: Fast Whitening of the Machine-Learning Black Box [1.7534486934148554]
  interpretability approaches should provide actionable insights without making the users wait. We propose Accelerated Model-agnostic Explanations (AcME), an interpretability approach that quickly provides feature importance scores both at the global and the local level. AcME computes feature ranking, but it also provides a what-if analysis tool to assess how changes in features values would affect model predictions.
  arXiv  Detail & Related papers  (2021-12-23T15:18:13Z)
• Modeling Generalization in Machine Learning: A Methodological and Computational Study [0.8057006406834467]
  We use the concept of the convex hull of the training data in assessing machine learning generalization. We observe unexpectedly weak associations between the generalization ability of machine learning models and all metrics related to dimensionality.
  arXiv  Detail & Related papers  (2020-06-28T19:06:16Z)
• Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study [81.11161697133095]
  We take the NER task as a testbed to analyze the generalization behavior of existing models from different perspectives. Experiments with in-depth analyses diagnose the bottleneck of existing neural NER models. As a by-product of this paper, we have open-sourced a project that involves a comprehensive summary of recent NER papers.
  arXiv  Detail & Related papers  (2020-01-12T04:33:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.