用鲁棒优化提升拉曼光谱分类准确率，对抗生物样本噪声
A Robust Support Vector Machine Approach for Raman COVID-19 Data Classification
- 基于范数有界不确定集构建鲁棒SVM，增强对噪声的抵抗力
- 在意大利医院真实数据上，分类准确率优于现有先进方法
- 适合处理含噪生物医学数据的分类任务，尤其适用于拉曼光谱
近年来，医疗技术进步带来了大量生物样本数据，尤其拉曼光谱在早期诊断中应用成功。但光谱本身复杂且变异大，手动分析困难，传统统计与机器学习方法难以保证准确可靠。本文提出一种新型鲁棒支持向量机（SVM）方法，针对拉曼光谱中的新冠样本分类问题，通过有界范数不确定性集构建鲁棒对偶模型，保护分类过程免受噪声干扰。研究涵盖线性与核函数分类器，支持二分类与多分类任务。在意大利医院提供的真实新冠数据集上验证，实验结果表明该方法性能优于当前先进分类器。
原文摘要 · Abstract (English)
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.
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