标题： 基于贝叶斯聚合的稀疏功能数据分类 显示英文标题

标题： Sparse Functional Data Classification via Bayesian Aggregation

摘要： 稀疏函数数据在现实世界的应用中经常出现，这对准确分类提出了重大挑战。 为了解决这个问题，我们提出了一种新的分类方法，将函数主成分分析（FPCA）与贝叶斯聚合相结合。 与传统的集成方法不同，我们的方法在自举副本上结合预测概率，并通过使用贝叶斯广义线性模型（贝叶斯GLMs）进行贝叶斯校准来优化它们。 我们评估了所提出方法相对于单一分类器和传统集成技术的性能。 模拟结果表明，贝叶斯聚合提高了分类准确性。 最后，我们通过三个真实数据分析验证了该方法。 显示英文摘要

摘要： Sparse functional data frequently arise in real-world applications, posing significant challenges for accurate classification. To address this, we propose a novel classification method that integrates functional principal component analysis (FPCA) with Bayesian aggregation. Unlike traditional ensemble methods, our approach combines predicted probabilities across bootstrap replicas and refines them through Bayesian calibration using Bayesian generalized linear models (Bayesian GLMs). We evaluated the performance of the proposed method against single classifiers and conventional ensemble techniques. The simulation results demonstrate that Bayesian aggregation improves the classification accuracy over conventional methods. Finally, we validate the approach through three real-data analyses.