A Weighted Random Forests Approach to Improve Predictive Performance.
A Weighted Random Forests Approach to Improve Predictive Performance.
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DOI：
10.1002/sam.11196
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发表时间：
2013-12-01
影响因子：
1.3
通讯作者：
Biernacka, Joanna M.
中科院分区：
文献类型：
--
作者：
Winham, Stacey J.;Freimuth, Robert R.;Biernacka, Joanna M.
关键词：
在高维数据中识别与复杂疾病相关的遗传变异是一个具有挑战性的问题，并且在分析中经常忽略诸如基因-基因相互作用等复杂的病因。数据挖掘方法随机森林（RF）可以处理高维；然而，在高维数据中，RF 并不是通过复杂的遗传模型（例如没有强边际成分的基因间相互作用）识别与疾病性状相关的风险因素的有效过滤器。在这里，我们提出了一种名为加权随机森林（wRF）的扩展，它结合了树级权重，以强调在变量重要性的预测和计算中更准确的树。我们通过模拟和对成瘾基因研究数据的应用证明，wRF 在高维数据中可以优于 RF，尽管改进幅度不大，并且仅限于效应大小大于复杂疾病遗传学中实际效应大小的情况。因此，目前的 wRF 实施不太可能改善高维遗传数据中相关预测变量的检测，但可能适用于预期效应量较大的其他情况。
Identifying genetic variants associated with complex disease in high-dimensional data is a challenging problem, and complicated etiologies such as gene-gene interactions are often ignored in analyses. The data-mining method Random Forests (RF) can handle high-dimensions; however, in high-dimensional data, RF is not an effective filter for identifying risk factors associated with the disease trait via complex genetic models such as gene-gene interactions without strong marginal components. Here we propose an extension called Weighted Random Forests (wRF), which incorporates tree-level weights to emphasize more accurate trees in prediction and calculation of variable importance. We demonstrate through simulation and application to data from a genetic study of addiction that wRF can outperform RF in high-dimensional data, although the improvements are modest and limited to situations with effect sizes that are larger than what is realistic in genetics of complex disease. Thus, the current implementation of wRF is unlikely to improve detection of relevant predictors in high-dimensional genetic data, but may be applicable in other situations where larger effect sizes are anticipated.
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影响因子：
3.4
作者：
Karpyak VM;Geske JR;Colby CL;Mrazek DA;Biernacka JM
通讯作者：
Biernacka JM
影响因子：
9.8
作者：
Culverhouse, R;Suarez, BK;Reich, T
通讯作者：
Reich, T
影响因子：
0.6
作者：
Kim, Hyunjoong;Kim, Hyeuk;Ahn, Hongshik
通讯作者：
Ahn, Hongshik
影响因子：
5.8
作者：
Amaratunga, Dhammika;Cabrera, Javier;Lee, Yung-Seop
通讯作者：
Lee, Yung-Seop
影响因子：
2.9
作者：
Lunetta KL;Hayward LB;Segal J;Van Eerdewegh P
通讯作者：
Van Eerdewegh P