Rotation Survival Forest for High Dimensional Censored Data

来源 :上海交通大学 | 被引量 : 0次 | 上传用户：zhuzihai

【摘要】

：

In this study,we extend rotation forest to high dimensional censored time-to-event data analysis by combing random subspace,bagging and rotation forest.

【作者】

：

Hong Wang Lifeng Zhou

【机构】

：

School of Mathematics and Statistics,Central South University,410075,China

【出处】

：

上海交通大学

【发表日期】

：

2016年4期

【关键词】

：

survival ensemble rotation forest censored data high-dimensional data

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　　In this study,we extend rotation forest to high dimensional censored time-to-event data analysis by combing random subspace,bagging and rotation forest.

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Rotation Survival Forest for High Dimensional Censored Data

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