论文标题
基于一致性的生存眼镜蛇与回归类型弱学习者
Concordance based Survival Cobra with regression type weak learners
论文作者
论文摘要
在本文中，我们通过合并的回归策略来预测条件生存功能。我们将弱的学习者视为不同的随机生存树。我们建议在右审核设置中最大化和解以找到最佳参数。我们探讨了两种方法，一种通常的生存眼镜蛇和基于一致性指数的新型加权预测指标。我们提出的配方使用两种不同的规范，例如Max-Norm和Frobenius Norm，从测试数据集中的查询点找到了一组邻近的预测。我们通过三个不同的现实生活数据集实现说明了算法。
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.
