Ordinal Forests
Ordinal Forests
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有序森林
DOI：
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发表时间：
2019
影响因子：
2
通讯作者：
R. Hornung
中科院分区：
文献类型：
--
作者：
R. Hornung
序数森林法是一种基于随机森林的序数响应变量预测方法。序数森林允许使用低维和高维协变量数据进行预测，并且可以另外用于根据协变量对预测的重要性对其进行排序。一项广泛的比较研究表明，在预测性能方面，有序森林往往优于竞争对手。此外，可以看出，有序森林目前使用的协变量重要性度量至少与竞争对手使用的度量相似，可以区分有影响的协变量和噪声协变量。在进一步的研究中，我们进一步研究了有序森林算法的几个重要性质。序数森林使用优化得分值代替序数响应变量的类值的基本原理原则上适用于除随机森林之外的任何回归方法，用于序数森林方法中考虑的连续结果。
The ordinal forest method is a random forest–based prediction method for ordinal response variables. Ordinal forests allow prediction using both low-dimensional and high-dimensional covariate data and can additionally be used to rank covariates with respect to their importance for prediction. An extensive comparison study reveals that ordinal forests tend to outperform competitors in terms of prediction performance. Moreover, it is seen that the covariate importance measure currently used by ordinal forest discriminates influential covariates from noise covariates at least similarly well as the measures used by competitors. Several further important properties of the ordinal forest algorithm are studied in additional investigations. The rationale underlying ordinal forests of using optimized score values in place of the class values of the ordinal response variable is in principle applicable to any regression method beyond random forests for continuous outcome that is considered in the ordinal forest method.