标题： 一种用于拉曼冠状病毒数据分类的鲁棒支持向量机方法 显示英文标题

标题： A Robust Support Vector Machine Approach for Raman COVID-19 Data Classification

摘要： 最近在医疗技术方面的进展使得多种技术和应用中可以获取大量生物样本。 特别是，在过去几年中，生物样本的拉曼光谱分析已被成功应用于早期诊断。 然而，光谱固有的复杂性和变异性使得手动分析具有挑战性，即使对于领域专家也是如此。 出于同样的原因，传统统计和机器学习（ML）技术无法保证准确和可靠的结果。 结合稳健优化技术的ML模型，提供了提高分类准确性和增强预测模型鲁棒性的可能性。 在本文中，我们研究了一种用于支持向量机（SVM）的新型稳健公式在分类从拉曼光谱获得的冠状病毒（COVID-19）样本中的性能。 鉴于生物样本的噪声和扰动性质，我们通过应用稳健优化技术来保护分类过程免受不确定性的影响。 具体来说，我们使用围绕每个观测值的范数有界不确定性集，推导了确定性公式的稳健对应模型。 我们探讨了线性和核诱导分类器的情况，以解决二元和多类分类任务。 我们的方法的有效性通过将模拟结果与最先进的分类器进行比较，在意大利医院提供的真实世界冠状病毒数据集上进行了验证。 显示英文摘要

摘要： Recent advances in healthcare technologies have led to the availability of large amounts of biological samples across several techniques and applications. In particular, in the last few years, Raman spectroscopy analysis of biological samples has been successfully applied for early-stage diagnosis. However, spectra' inherent complexity and variability make the manual analysis challenging, even for domain experts. For the same reason, the use of traditional Statistical and Machine Learning (ML) techniques could not guarantee for accurate and reliable results. ML models, combined with robust optimization techniques, offer the possibility to improve the classification accuracy and enhance the resilience of predictive models. In this paper, we investigate the performance of a novel robust formulation for Support Vector Machine (SVM) in classifying COVID-19 samples obtained from Raman Spectroscopy. Given the noisy and perturbed nature of biological samples, we protect the classification process against uncertainty through the application of robust optimization techniques. Specifically, we derive robust counterpart models of deterministic formulations using bounded-by-norm uncertainty sets around each observation. We explore the cases of both linear and kernel-induced classifiers to address binary and multiclass classification tasks. The effectiveness of our approach is validated on real-world COVID-19 datasets provided by Italian hospitals by comparing the results of our simulations with a state-of-the-art classifier.