The implementation of random survival forests in conflict management data: An examination of power sharing and third party mediation in post-conflict countries.
The implementation of random survival forests in conflict management data: An examination of power sharing and third party mediation in post-conflict countries.
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DOI：
10.1371/journal.pone.0250963
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Cann D
中科院分区：
文献类型：
--
作者：
Whetten AB;Stevens JR;Cann D
时间-事件分析是政治学中常见的现象。近年来，机器学习方法在定量政治学研究中的使用越来越多。本文提倡实现机器学习持续时间模型，以帮助建立合理的模型选择过程。我们提供了一个简短的教程介绍随机生存森林（RSF）算法，并将其与一个流行的前辈，考克斯比例风险模型，强调方法效用的政治学研究人员。我们为模拟的时间到事件数据和功率共享事件数据集（PSED）实现了这两种方法，以帮助研究人员评估机器学习持续时间模型的优点。我们提供的证据显着更高的生存概率的和平协议与第三方调解的设计和实施。我们还发现，纳入领土权力分享和避免多个反叛政党签署的和平协议的生存概率增加。此外，RSF，一个以前未充分使用的方法，用于分析政治学时间到事件的数据，提供了一种新的方法，排名的和平协议标准的重要性，在预测和平协议的持续时间。我们的研究结果展示了一个场景，展示了RSF的政治科学时间到事件数据的可解释性和性能。这些发现证明了随机生存森林算法在许多情况下具有强大的可解释性和竞争力，此外还促进了时间到事件政治学数据的多样化，整体模型选择过程。
Time-to-event analysis is a common occurrence in political science. In recent years, there has been an increased usage of machine learning methods in quantitative political science research. This article advocates for the implementation of machine learning duration models to assist in a sound model selection process. We provide a brief tutorial introduction to the random survival forest (RSF) algorithm and contrast it to a popular predecessor, the Cox proportional hazards model, with emphasis on methodological utility for political science researchers. We implement both methods for simulated time-to-event data and the Power-Sharing Event Dataset (PSED) to assist researchers in evaluating the merits of machine learning duration models. We provide evidence of significantly higher survival probabilities for peace agreements with 3rd party mediated design and implementation. We also detect increased survival probabilities for peace agreements that incorporate territorial power-sharing and avoid multiple rebel party signatories. Further, the RSF, a previously under-used method for analyzing political science time-to event data, provides a novel approach for ranking of peace agreement criteria importance in predicting peace agreement duration. Our findings demonstrate a scenario exhibiting the interpretability and performance of RSF for political science time-to-event data. These findings justify the robust interpretability and competitive performance of the random survival forest algorithm in numerous circumstances, in addition to promoting a diverse, holistic model-selection process for time-to-event political science data.
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DOI：
10.1198/016214506000000438
发表时间：
2006-09-01
影响因子：
3.7
作者：
Fan, Juanjuan;Su, Xiao-Gang;LeBlanc, Michael
通讯作者：
LeBlanc, Michael
影响因子：
4
作者：
Chen HC;Kodell RL;Cheng KF;Chen JJ
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影响因子：
2.2
作者：
Gregorutti, Baptiste;Michel, Bertrand;Saint-Pierre, Philippe
通讯作者：
Saint-Pierre, Philippe
影响因子：
5.8
作者：
Calhoun, Peter;Su, Xiaogang;Fan, Juanjuan
通讯作者：
Fan, Juanjuan
影响因子：
3.6
作者：
Schrodt, Philip A.
通讯作者：
Schrodt, Philip A.