In this paper, we predict conditional survival functions through a combined regression strategy. We take weak learners as different random survival trees. We propose to maximize concordance in the right-censored set up to find the optimal parameters. We explore two approaches, a usual survival cobra and a novel weighted predictor based on the concordance index. Our proposed formulations use two different norms, say, Max-norm and Frobenius norm, to find a proximity set of predictions from query points in the test dataset. We illustrate our algorithms through three different real-life dataset implementations.
翻译:在本文中,我们通过综合回归策略预测有条件生存功能。我们把弱小学习者视为不同的随机生存树。我们建议最大限度地实现为寻找最佳参数而设置的右检查系统的一致性。我们探索两种方法,一种通常的生存眼镜蛇和一种基于和谐指数的新颖加权预测。我们提议的配方使用两种不同的规范,例如马克斯-诺姆和弗罗贝尼乌斯规范,从测试数据集的查询点中找到一套相近的预测。我们通过三种不同的真实数据集实施方式来说明我们的算法。