贝叶斯 Kolmogorov Arnold Networks（Bayesian_KANs）：一种提升准确性和可解释性的概率方法
Bayesian Kolmogorov Arnold Networks (Bayesian_KANs): A Probabilistic Approach to Enhance Accuracy and Interpretability
AI总结：
本文提出 BKANs 框架，将 Kolmogorov Arnold Networks 与贝叶斯推断结合，在两个医学诊断基准上提升准确性、可解释性和不确定性量化。
摘要：
由于其强大的预测能力，深度学习已成为包括医疗保健在内的许多行业中不可或缺的工具。然而，传统深度学习模型往往缺乏可解释性，并且忽略了对预测不确定性的考虑，而这正是临床决策中的两个关键组成部分。为了生成可解释且具有不确定性感知的预测，本研究提出了一个名为 Bayesian Kolmogorov Arnold Networks（BKANs）的新颖框架，该框架将 Kolmogorov Arnold Networks 的表达能力与贝叶斯推断相结合。我们在两个医学数据集上应用 BKANs：Pima Indians Diabetes 数据集和 Cleveland Heart Disease 数据集，它们是评估医学诊断中机器学习模型的广泛使用基准。我们的方法在预测准确性方面优于传统深度学习模型，并提供了关于预测置信度和决策边界的有用洞见。此外，BKANs 表示偶然不确定性和认知不确定性的能力，确保医生获得更加可靠和值得信赖的决策支持。实验结果表明，我们的贝叶斯策略提高了模型的可解释性，并显著减少了过拟合，这对于小型且不平衡的医学数据集非常重要。我们提出了可能的扩展，以在更复杂的多模态数据集中进一步利用 BKANs，并论述了这些发现对于未来构建可靠医疗保健 AI 系统研究的意义。这项工作为在透明度和可靠性至关重要的关键领域部署深度学习模型的新范式铺平了道路。
英文摘要：
Because of its strong predictive skills, deep learning has emerged as an essential tool in many industries, including healthcare. Traditional deep learning models, on the other hand, frequently lack interpretability and omit to take prediction uncertainty into account two crucial components of clinical decision making. In order to produce explainable and uncertainty aware predictions, this study presents a novel framework called Bayesian Kolmogorov Arnold Networks (BKANs), which combines the expressive capacity of Kolmogorov Arnold Networks with Bayesian inference. We employ BKANs on two medical datasets, which are widely used benchmarks for assessing machine learning models in medical diagnostics: the Pima Indians Diabetes dataset and the Cleveland Heart Disease dataset. Our method provides useful insights into prediction confidence and decision boundaries and outperforms traditional deep learning models in terms of prediction accuracy. Moreover, BKANs' capacity to represent aleatoric and epistemic uncertainty guarantees doctors receive more solid and trustworthy decision support. Our Bayesian strategy improves the interpretability of the model and considerably minimises overfitting, which is important for tiny and imbalanced medical datasets, according to experimental results. We present possible expansions to further use BKANs in more complicated multimodal datasets and address the significance of these discoveries for future research in building reliable AI systems for healthcare. This work paves the way for a new paradigm in deep learning model deployment in vital sectors where transparency and reliability are crucial.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
