Relevance Vector Machine for Survival Analysis

Kiaee, Farkhondeh; Sheikhzadeh, Hamid; Mahabadi, Samaneh Eftekhari

doi:10.1109/tnnls.2015.2420611

Cited by 31 publications

(8 citation statements)

References 21 publications

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“…Relevance Vector Machine (RVM) [Widodo and Yang 2011;Kiaee et al 2016], which obtains the parsimonious estimations for regression and probabilistic problems using Bayesian inference, has the same formulation as SVM but provides probabilistic classification. RVM adopts a Bayesian approach by considering the prior over the weights controlled by some parameters.…”

Section: Support Vector Machinesmentioning

confidence: 99%

Machine Learning for Survival Analysis

2019

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Survival analysis is a subfield of statistics where the goal is to analyze and model the data where the outcome is the time until the occurrence of an event of interest. One of the main challenges in this context is the presence of instances whose event outcomes become unobservable after a certain time point or when some instances do not experience any event during the monitoring period. Such a phenomenon is called censoring which can be effectively handled using survival analysis techniques. Traditionally, statistical approaches have been widely developed in the literature to overcome this censoring issue. In addition, many machine learning algorithms are adapted to effectively handle survival data and tackle other challenging problems that arise in real-world data. In this survey, we provide a comprehensive and structured review of the representative statistical methods along with the machine learning techniques used in survival analysis and provide a detailed taxonomy of the existing methods. We also discuss several topics that are closely related to survival analysis and illustrate several successful applications in various real-world application domains. We hope that this paper will provide a more thorough understanding of the recent advances in survival analysis and offer some guidelines on applying these approaches to solve new problems that arise in applications with censored data.

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Section: Support Vector Machinesmentioning

confidence: 99%

Machine Learning for Survival Analysis

2019

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“…The bene ts were: 1) increased sparsity, which improves generalization and prevents over tting; and 2) automatic relevance sample selection based on data, which increases accuracy, especially for heavily censored survival data. In the simulation, They used the Cox PH model, Kaplan-Meier curves, accelerated failure time (AFT) model and non-Mercer kernels, Bayesian kernel, Gaussian kernel, regression-based kernel survival models about pancreas-head cancer, lung cancer, prostatic cancer, and breast cancer (34).…”

Section: Multiple Kernel Learningmentioning

confidence: 99%

Application of Kernel-based Learning Algorithms in Survival Analysis: A Systematic Review

Rezaei

Montaseri

Mostafaei

et al. 2023

Preprint

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Background The time until an event happens is the outcome variable of interest in the statistical data analysis method known as survival analysis. Some researchers have created kernel statistics for various types of data and kernels that allow the association of a set of markers with survival data. Multiple Kernel Learning (MKL) is often considered a linear or convex combination of multiple kernels. This paper aims to provide a comprehensive overview of the application of kernel learning algorithms in survival analysis. Methods We conducted a systematic review which involved an extensive search for relevant literature in the field of biomedicine. After using the keywords in literature searching, 435 articles were identified based on the title and abstract screening. Result In this review, out of a total of 56 selected articles, only 20 articles that have used MKL for high-dimensional data, were included. In most of these articles, the MKL method has been expanded and has been introduced as a novel method. In these studies, the extended MKL models due to the nature of classification or regression have been compared with SVM, Cox PH (Cox), Extreme Learning (ELM), MKCox, Gradient Boosting (GBCox), Parametric Censored Regression Models (PCRM), Elastic-net Cox (EN-Cox), LASSO-Cox, Random Survival Forests (RSF), and Boosting Concordance Index (BoostCI). In most of these articles, the optimal model’s parameters are estimated by 10-fold cross-validation. In addition, the Concordance index (C-index) and the area under the ROC curve (AUC) were calculated to quantitatively measure the performance of all methods for validation. Predictive accuracy is improved by using kernels. Conclusion Our findings suggest that using multiple kernels instead of one single kernel can make decision functions more interpretable and can improve performance.

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“…Despite these recent advances, the proportional hazards framework still requires the inclusion of time-dependencies to the proportionality factor. A relevance vector machine extension of the AFT model was proposed by [31]. In this model, the survival time is restricted to a Weibull probability distribution.…”

Section: Introductionmentioning

confidence: 99%

Metaparametric Neural Networks for Survival Analysis

Mello

Wilkinson

Kadirkamanathan

2023

IEEE Trans. Neural Netw. Learning Syst.

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Survival analysis is a critical tool for the modelling of time-to-event data, such as life expectancy after a cancer diagnosis or optimal maintenance scheduling for complex machinery. However, current neural network models provide an imperfect solution for survival analysis as they either restrict the shape of the target probability distribution or restrict the estimation to pre-determined times. As a consequence, current survival neural networks lack the ability to estimate a generic function without prior knowledge of its structure. In this article, we present the metaparametric neural network framework that encompasses existing survival analysis methods and enables their extension to solve the aforementioned issues. This framework allows survival neural networks to satisfy the same independence of generic function estimation from the underlying data structure that characterizes their regression and classification counterparts. Further, we demonstrate the application of the metaparametric framework using both simulated and large real-world datasets and show that it outperforms the current state-of-the-art methods in (i) capturing nonlinearities, and (ii) identifying temporal patterns, leading to more accurate overall estimations whilst placing no restrictions on the underlying function structure.

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Relevance Vector Machine for Survival Analysis

Cited by 31 publications

References 21 publications

Machine Learning for Survival Analysis

Machine Learning for Survival Analysis

Application of Kernel-based Learning Algorithms in Survival Analysis: A Systematic Review

Metaparametric Neural Networks for Survival Analysis

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