BoostHD通过提升高维计算的利用率，显著增强医疗数据模型的可靠性。
Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare
- 将提升算法引入高维计算，分块构建弱学习器集成
- 在WESAD数据集上达98.37%准确率，优于随机森林等模型
- 适合对精度与稳定性要求高的医疗场景应用
高维计算（HDC）在高维空间中实现高效的数据编码与处理，适用于机器学习与数据分析。然而，空间利用不充分会导致过拟合和模型可靠性下降，尤其在数据有限的医疗领域这一问题尤为突出。本文提出BoostHD，通过提升算法将高维空间划分为子空间，构建弱学习器集成。结合提升与HDC，BoostHD在性能与可靠性上超越现有方法。实验表明，在WESAD数据集上准确率达98.37%，超过随机森林、XGBoost与OnlineHD。BoostHD还展现出优异的推理效率与稳定性，在数据不平衡与噪声环境下仍保持高精度。个体特异性评估中，平均准确率为96.19%，优于其他模型。该方法解决了提升与HDC各自的局限，拓展了其在高可靠性要求领域的应用。
原文摘要 · Abstract (English)
Hyperdimensional computing (HDC) enables efficient data encoding and processing in high-dimensional space, benefiting machine learning and data analysis. However, underutilization of these spaces can lead to overfitting and reduced model reliability, especially in data-limited systems a critical issue in sectors like healthcare that demand robustness and consistent performance. We introduce BoostHD, an approach that applies boosting algorithms to partition the hyperdimensional space into subspaces, creating an ensemble of weak learners. By integrating boosting with HDC, BoostHD enhances performance and reliability beyond existing HDC methods. Our analysis highlights the importance of efficient utilization of hyperdimensional spaces for improved model performance. Experiments on healthcare datasets show that BoostHD outperforms state-of-the-art methods. On the WESAD dataset, it achieved an accuracy of 98.37%, surpassing Random Forest, XGBoost, and OnlineHD. BoostHD also demonstrated superior inference efficiency and stability, maintaining high accuracy under data imbalance and noise. In person-specific evaluations, it achieved an average accuracy of 96.19%, outperforming other models. By addressing the limitations of both boosting and HDC, BoostHD expands the applicability of HDC in critical domains where reliability and precision are paramount.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。