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Gradient-based kernel variable selection for support vector hazards machine

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Abstract

This study aims to improve the predictive performance for the event time through the machine learning model and find informative variables in the time-to-event data, simultaneously. To address this issue, after regarding the time-to-event data as the dichotomized counting processes data for predicting survival time, we consider the time-dependent support vector machine (SVM) framework for the dichotomized counting process data, where the decision function in this framework consists of the time-independent risk score and time-dependent intercept. Also, we consider the empirical partial derivative of the risk score function with respect to each marginal predictor as the indicator for the important predictor. Through this approach, it is possible to predict survival time and find variables that affect on the survival time at the same time. Simulation studies were conducted to confirm the performance of the model, and real data analysis was conducted by predicting the survival time of the lung cancer after the diagnosis and selecting genes associate with lung cancer through human gene data.

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Data availability

We used Beer’s microarray data, which is available with LungCancer3 function in the R package “GSCA”.

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Acknowledgements

We thank the Editor, Associate Editor and two reviewers, whose questions and insightful comments have led to a much improved paper.

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF2021R1C1C1007023).

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Authors and Affiliations

  1. Department of Statistics, Pusan National University, Busan, South Korea

    Sanghun Jeong, Kyungjun Kang & Hojin Yang

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  1. Sanghun Jeong
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  2. Kyungjun Kang
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  3. Hojin Yang
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Correspondence to Hojin Yang.

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Jeong, S., Kang, K. & Yang, H. Gradient-based kernel variable selection for support vector hazards machine. J. Korean Stat. Soc. 53, 509–536 (2024). https://doi.org/10.1007/s42952-024-00256-5

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