PUBLICATION DATE

2017-09-13

AUTHORS

Hong Wang, Lifeng Zhou, Jianxin Wang

TITLE

A survival ensemble of extreme learning machine

ISSUE

N/A

VOLUME

N/A

ISSN (print)

0924-669X

ISSN (electronic)

1573-7497

ABSTRACT

Due to the fast learning speed, simplicity of implementation and minimal human intervention, extreme learning machine has received considerable attentions recently, mostly from the machine learning community. Generally, extreme learning machine and its various variants focus on classification and regression problems. Its potential application in analyzing censored time-to-event data is yet to be verified. In this study, we present an extreme learning machine ensemble to model right-censored survival data by combining the Buckley-James transformation and the random forest framework. According to experimental and statistical analysis results, we show that the proposed model outperforms popular survival models such as random survival forest, Cox proportional hazard models on well-known low-dimensional and high-dimensional benchmark datasets in terms of both prediction accuracy and time efficiency.

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32 TRIPLES      25 PREDICATES      33 URIs      16 LITERALS

Subject Predicate Object
1 articles:4712827ea76c0eb233c7fd36f9636629 sg:abstract Abstract Due to the fast learning speed, simplicity of implementation and minimal human intervention, extreme learning machine has received considerable attentions recently, mostly from the machine learning community. Generally, extreme learning machine and its various variants focus on classification and regression problems. Its potential application in analyzing censored time-to-event data is yet to be verified. In this study, we present an extreme learning machine ensemble to model right-censored survival data by combining the Buckley-James transformation and the random forest framework. According to experimental and statistical analysis results, we show that the proposed model outperforms popular survival models such as random survival forest, Cox proportional hazard models on well-known low-dimensional and high-dimensional benchmark datasets in terms of both prediction accuracy and time efficiency.
2 sg:articleType OriginalPaper
3 sg:ddsId s10489-017-1063-4
4 sg:ddsIdJournalBrand 10489
5 sg:doi 10.1007/s10489-017-1063-4
6 sg:doiLink http://dx.doi.org/10.1007/s10489-017-1063-4
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19 sg:issnElectronic 1573-7497
20 sg:issnPrint 0924-669X
21 sg:language English
22 sg:license http://scigraph.springernature.com/explorer/license/
23 sg:pageEnd 13
24 sg:pageStart 1
25 sg:publicationDate 2017-09-13
26 sg:publicationYear 2017
27 sg:publicationYearMonth 2017-09
28 sg:scigraphId 4712827ea76c0eb233c7fd36f9636629
29 sg:title A survival ensemble of extreme learning machine
30 sg:webpage https://link.springer.com/10.1007/s10489-017-1063-4
31 rdf:type sg:Article
32 rdfs:label Article: A survival ensemble of extreme learning machine
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