用自注意力机制融合生存预测，提升模型稳定性与准确性
Ensemble-Based Survival Models with the Self-Attended Beran Estimator Predictions
- 引入自注意力机制整合多个生存函数预测
- 在多个数据集上优于现有最先进模型
- 适合医疗时间事件预测等场景使用
生存分析旨在预测事件（如故障或死亡）发生的时间，但常受删失数据影响，即部分事件未被观测到。基于集成的方法（如随机生存森林和梯度提升）虽广泛应用，但因自助采样差异导致预测不稳定。为此，我们提出 SurvBESA（Survival Beran Estimators Self-Attended），一种结合 Beran 估计器与自注意力机制的新集成模型。不同于传统方法，SurvBESA 利用自注意力对预测的生存函数进行加权融合，通过相似性调整每个函数的权重，从而平滑噪声。我们还探索了基于 Huber 污染模型定义注意力权重的特例，使训练简化为二次或线性优化问题。数值实验表明，SurvBESA 在多个基准数据集上超越现有最优模型。代码已公开。
原文摘要 · Abstract (English)
Survival analysis predicts the time until an event of interest, such as failure or death, but faces challenges due to censored data, where some events remain unobserved. Ensemble-based models, like random survival forests and gradient boosting, are widely used but can produce unstable predictions due to variations in bootstrap samples. To address this, we propose SurvBESA (Survival Beran Estimators Self-Attended), a novel ensemble model that combines Beran estimators with a self-attention mechanism. Unlike traditional methods, SurvBESA applies self-attention to predicted survival functions, smoothing out noise by adjusting each survival function based on its similarity to neighboring survival functions. We also explore a special case using Huber's contamination model to define attention weights, simplifying training to a quadratic or linear optimization problem. Numerical experiments show that SurvBESA outperforms state-of-the-art models. The implementation of SurvBESA is publicly available.
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