A Multi-task Kernel Learning Algorithm for Survival Analysis

Meng, Z; Xu, J; Li, Z; Wang, Y; Chen, F; Wang, Z

A Multi-task Kernel Learning Algorithm for Survival Analysis

Meng, Z Xu, J Li, Z

Wang, Y

Chen, F

Wang, Z

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Advances in Knowledge Discovery and Data Mining, 2021, 12714 LNAI, pp. 298-311
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Meng, Z
dc.contributor.author	Xu, J
dc.contributor.author	Li, Z https://orcid.org/0000-0002-0784-157X
dc.contributor.author	Wang, Y https://orcid.org/0000-0002-6815-0879
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.contributor.author	Wang, Z
dc.contributor.editor	Karlapalem, K
dc.contributor.editor	Cheng, H
dc.contributor.editor	Ramakrishnan, N
dc.contributor.editor	Agrawal, RK
dc.contributor.editor	Reddy, PK
dc.contributor.editor	Srivastava, J
dc.contributor.editor	Chakraborty, T
dc.date	2021-05-11
dc.date.accessioned	2022-05-24T07:27:31Z
dc.date.available	2022-05-24T07:27:31Z
dc.date.issued	2021-01-01
dc.identifier.citation	Advances in Knowledge Discovery and Data Mining, 2021, 12714 LNAI, pp. 298-311
dc.identifier.isbn	9783030757670
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/157674
dc.description.abstract	Survival analysis aims to predict the occurring times of certain events of interest. Most existing methods for survival analysis either assume specific forms for the underlying stochastic processes or linear hypotheses. To cope with non-linearity in data, we propose a unified framework that combines multi-task and kernel learning for survival analysis. We also develop optimization methods based on the Pegasos (Primal estimated sub-gradient solver for SVM) algorithm for learning. Experiment results demonstrate the effectiveness of the proposed method for survival analysis, on synthetic and real-world data sets.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Advances in Knowledge Discovery and Data Mining
dc.relation.ispartof	Pacific-Asia Conference on Knowledge Discovery and Data Mining
dc.relation.ispartofseries	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.isbasedon	10.1007/978-3-030-75768-7_24
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	A Multi-task Kernel Learning Algorithm for Survival Analysis
dc.type	Conference Proceeding
utslib.citation.volume	12714 LNAI
utslib.location.activity	Virtual Event
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-05-24T07:27:30Z
pubs.finish-date	2021-05-14
pubs.place-of-publication	Cham, Switzerland
pubs.publication-status	Published
pubs.start-date	2021-05-11
pubs.volume	12714 LNAI
dc.location	Cham, Switzerland

Abstract:

Survival analysis aims to predict the occurring times of certain events of interest. Most existing methods for survival analysis either assume specific forms for the underlying stochastic processes or linear hypotheses. To cope with non-linearity in data, we propose a unified framework that combines multi-task and kernel learning for survival analysis. We also develop optimization methods based on the Pegasos (Primal estimated sub-gradient solver for SVM) algorithm for learning. Experiment results demonstrate the effectiveness of the proposed method for survival analysis, on synthetic and real-world data sets.

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http://hdl.handle.net/10453/157674