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Random rotation survival forest for high dimensional censored data.

Lifeng Zhou1, Hong Wang1, Qingsong Xu1

  • 1School of Mathematics and Statistics, Central South University, South Shaoshan Road, Changsha, 410075 Hunan China.

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|September 15, 2016
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Summary

This study introduces random rotation survival forest, a new method for analyzing high-dimensional survival data. It effectively overcomes computational limitations of traditional rotation forest, outperforming existing survival models.

Keywords:
Censored dataHigh-dimensional dataRotation forestSurvival ensembleTime-to-event data

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Area of Science:

  • Machine Learning
  • Biostatistics
  • Survival Analysis

Background:

  • Rotation forest, a machine learning ensemble, has been adapted for regression and survival analysis.
  • High-dimensional and big data present computational challenges for standard rotation forest due to principal component analysis.
  • Existing methods struggle with efficient analysis of complex, high-dimensional time-to-event data.

Purpose of the Study:

  • To extend rotation forest methodology for analyzing high-dimensional censored time-to-event data.
  • To develop a computationally efficient and accurate survival analysis model for big data.
  • To introduce the random rotation survival forest (RRSF) method.

Main Methods:

  • Combines random subspace, bagging, and rotation forest principles.
  • Applies principal component analysis within a random subspace framework to mitigate computational burden.
  • Utilizes ensemble learning for robust survival prediction.

Main Results:

  • The proposed random rotation survival forest (RRSF) demonstrates superior performance compared to existing methods.
  • RRSF effectively handles high-dimensional censored survival data.
  • Statistical analysis confirms the outperformance of RRSF against random survival forest and regularized Cox models.

Conclusions:

  • Random rotation survival forest is a powerful new tool for high-dimensional survival data analysis.
  • The method offers a computationally efficient alternative to traditional approaches.
  • RRSF advances the field of survival ensemble methods for complex datasets.