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On the use of multi-objective evolutionary algorithms for survival analysis.

Abstract This paper proposes and evaluates a multi-objective evolutionary algorithm for survival analysis. One aim of survival analysis is the extraction of models from data that approximate lifetime/failure time distributions. These models can be used to estimate the time that an event takes to happen to an object. To use of multi-objective evolutionary algorithms for survival analysis has several advantages. They can cope with feature interactions, noisy data, and are capable of optimising several objectives. This is important, as model extraction is a multi-objective problem. It has at least two objectives, which are the extraction of accurate and simple models. Accurate models are required to achieve good predictions. Simple models are important to prevent overfitting, improve the transparency of the models, and to save computational resources. Although there is a plethora of evolutionary approaches to extract models for classification and regression, the presented approach is one of the first applied to survival analysis. The approach is evaluated on several artificial datasets and one medical dataset. It is shown that the approach is capable of producing accurate models, even for problems that violate some of the assumptions made by classical approaches.
PMID
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Authors

Mayor MeshTerms

Algorithms

Survival Analysis

Keywords
Journal Title bio systems
Publication Year Start
%A Setzkorn, Christian; Taktak, Azzam F. G.; Damato, Bertil E.
%T On the use of multi-objective evolutionary algorithms for survival analysis.
%J Bio Systems, vol. 87, no. 1, pp. 31-48
%D 01/2007
%V 87
%N 1
%M eng
%B This paper proposes and evaluates a multi-objective evolutionary algorithm for survival analysis. One aim of survival analysis is the extraction of models from data that approximate lifetime/failure time distributions. These models can be used to estimate the time that an event takes to happen to an object. To use of multi-objective evolutionary algorithms for survival analysis has several advantages. They can cope with feature interactions, noisy data, and are capable of optimising several objectives. This is important, as model extraction is a multi-objective problem. It has at least two objectives, which are the extraction of accurate and simple models. Accurate models are required to achieve good predictions. Simple models are important to prevent overfitting, improve the transparency of the models, and to save computational resources. Although there is a plethora of evolutionary approaches to extract models for classification and regression, the presented approach is one of the first applied to survival analysis. The approach is evaluated on several artificial datasets and one medical dataset. It is shown that the approach is capable of producing accurate models, even for problems that violate some of the assumptions made by classical approaches.
%K Algorithms, Evaluation Studies as Topic, Models, Molecular, Survival Analysis
%P 31
%L 48
%Y 10.1016/j.biosystems.2006.03.002
%W PHY
%G AUTHOR
%R 2007.......87...31S

@Article{Setzkorn2007,
author="Setzkorn, Christian
and Taktak, Azzam F. G.
and Damato, Bertil E.",
title="On the use of multi-objective evolutionary algorithms for survival analysis.",
journal="Bio Systems",
year="2007",
month="Jan",
day="13",
volume="87",
number="1",
pages="31--48",
keywords="Algorithms",
keywords="Evaluation Studies as Topic",
keywords="Models, Molecular",
keywords="Survival Analysis",
abstract="This paper proposes and evaluates a multi-objective evolutionary algorithm for survival analysis. One aim of survival analysis is the extraction of models from data that approximate lifetime/failure time distributions. These models can be used to estimate the time that an event takes to happen to an object. To use of multi-objective evolutionary algorithms for survival analysis has several advantages. They can cope with feature interactions, noisy data, and are capable of optimising several objectives. This is important, as model extraction is a multi-objective problem. It has at least two objectives, which are the extraction of accurate and simple models. Accurate models are required to achieve good predictions. Simple models are important to prevent overfitting, improve the transparency of the models, and to save computational resources. Although there is a plethora of evolutionary approaches to extract models for classification and regression, the presented approach is one of the first applied to survival analysis. The approach is evaluated on several artificial datasets and one medical dataset. It is shown that the approach is capable of producing accurate models, even for problems that violate some of the assumptions made by classical approaches.",
issn="0303-2647",
doi="10.1016/j.biosystems.2006.03.002",
url="http://www.ncbi.nlm.nih.gov/pubmed/16762491",
language="eng"
}

%0 Journal Article
%T On the use of multi-objective evolutionary algorithms for survival analysis.
%A Setzkorn, Christian
%A Taktak, Azzam F. G.
%A Damato, Bertil E.
%J Bio Systems
%D 2007
%8 Jan 13
%V 87
%N 1
%@ 0303-2647
%G eng
%F Setzkorn2007
%X This paper proposes and evaluates a multi-objective evolutionary algorithm for survival analysis. One aim of survival analysis is the extraction of models from data that approximate lifetime/failure time distributions. These models can be used to estimate the time that an event takes to happen to an object. To use of multi-objective evolutionary algorithms for survival analysis has several advantages. They can cope with feature interactions, noisy data, and are capable of optimising several objectives. This is important, as model extraction is a multi-objective problem. It has at least two objectives, which are the extraction of accurate and simple models. Accurate models are required to achieve good predictions. Simple models are important to prevent overfitting, improve the transparency of the models, and to save computational resources. Although there is a plethora of evolutionary approaches to extract models for classification and regression, the presented approach is one of the first applied to survival analysis. The approach is evaluated on several artificial datasets and one medical dataset. It is shown that the approach is capable of producing accurate models, even for problems that violate some of the assumptions made by classical approaches.
%K Algorithms
%K Evaluation Studies as Topic
%K Models, Molecular
%K Survival Analysis
%U http://dx.doi.org/10.1016/j.biosystems.2006.03.002
%U http://www.ncbi.nlm.nih.gov/pubmed/16762491
%P 31-48

PT Journal
AU Setzkorn, C
   Taktak, AFG
   Damato, BE
TI On the use of multi-objective evolutionary algorithms for survival analysis.
SO Bio Systems
JI BioSystems
PD Jan
PY 2007
BP 31
EP 48
VL 87
IS 1
DI 10.1016/j.biosystems.2006.03.002
LA eng
DE Algorithms; Evaluation Studies as Topic; Models, Molecular; Survival Analysis
AB This paper proposes and evaluates a multi-objective evolutionary algorithm for survival analysis. One aim of survival analysis is the extraction of models from data that approximate lifetime/failure time distributions. These models can be used to estimate the time that an event takes to happen to an object. To use of multi-objective evolutionary algorithms for survival analysis has several advantages. They can cope with feature interactions, noisy data, and are capable of optimising several objectives. This is important, as model extraction is a multi-objective problem. It has at least two objectives, which are the extraction of accurate and simple models. Accurate models are required to achieve good predictions. Simple models are important to prevent overfitting, improve the transparency of the models, and to save computational resources. Although there is a plethora of evolutionary approaches to extract models for classification and regression, the presented approach is one of the first applied to survival analysis. The approach is evaluated on several artificial datasets and one medical dataset. It is shown that the approach is capable of producing accurate models, even for problems that violate some of the assumptions made by classical approaches.
ER

PMID- 16762491
OWN - NLM
STAT- MEDLINE
DA  - 20061218
DCOM- 20070222
LR  - 20071115
IS  - 0303-2647 (Print)
IS  - 0303-2647 (Linking)
VI  - 87
IP  - 1
DP  - 2007 Jan
TI  - On the use of multi-objective evolutionary algorithms for survival analysis.
PG  - 31-48
AB  - This paper proposes and evaluates a multi-objective evolutionary algorithm for
      survival analysis. One aim of survival analysis is the extraction of models from 
      data that approximate lifetime/failure time distributions. These models can be
      used to estimate the time that an event takes to happen to an object. To use of
      multi-objective evolutionary algorithms for survival analysis has several
      advantages. They can cope with feature interactions, noisy data, and are capable 
      of optimising several objectives. This is important, as model extraction is a
      multi-objective problem. It has at least two objectives, which are the extraction
      of accurate and simple models. Accurate models are required to achieve good
      predictions. Simple models are important to prevent overfitting, improve the
      transparency of the models, and to save computational resources. Although there
      is a plethora of evolutionary approaches to extract models for classification and
      regression, the presented approach is one of the first applied to survival
      analysis. The approach is evaluated on several artificial datasets and one
      medical dataset. It is shown that the approach is capable of producing accurate
      models, even for problems that violate some of the assumptions made by classical 
      approaches.
FAU - Setzkorn, Christian
AU  - Setzkorn C
AD  - Royal Liverpool University Hospital, Liverpool, United Kingdom.
      [email protected]
FAU - Taktak, Azzam F G
AU  - Taktak AF
FAU - Damato, Bertil E
AU  - Damato BE
LA  - eng
PT  - Journal Article
DEP - 20060313
PL  - Ireland
TA  - Biosystems
JT  - Bio Systems
JID - 0430773
SB  - IM
MH  - *Algorithms
MH  - Evaluation Studies as Topic
MH  - Models, Molecular
MH  - *Survival Analysis
EDAT- 2006/06/10 09:00
MHDA- 2007/02/23 09:00
CRDT- 2006/06/10 09:00
PHST- 2005/12/07 [received]
PHST- 2006/03/03 [revised]
PHST- 2006/03/03 [accepted]
PHST- 2006/03/13 [aheadofprint]
AID - S0303-2647(06)00046-3 [pii]
AID - 10.1016/j.biosystems.2006.03.002 [doi]
PST - ppublish
SO  - Biosystems. 2007 Jan;87(1):31-48. Epub 2006 Mar 13.
TY  - JOUR
AU  - Setzkorn, Christian
AU  - Taktak, Azzam F. G.
AU  - Damato, Bertil E.
PY  - 2007/Jan/13
TI  - On the use of multi-objective evolutionary algorithms for survival analysis.
T2  - BioSystems
JO  - Bio Systems
SP  - 31
EP  - 48
VL  - 87
IS  - 1
KW  - Algorithms
KW  - Evaluation Studies as Topic
KW  - Models, Molecular
KW  - Survival Analysis
N2  - This paper proposes and evaluates a multi-objective evolutionary algorithm for survival analysis. One aim of survival analysis is the extraction of models from data that approximate lifetime/failure time distributions. These models can be used to estimate the time that an event takes to happen to an object. To use of multi-objective evolutionary algorithms for survival analysis has several advantages. They can cope with feature interactions, noisy data, and are capable of optimising several objectives. This is important, as model extraction is a multi-objective problem. It has at least two objectives, which are the extraction of accurate and simple models. Accurate models are required to achieve good predictions. Simple models are important to prevent overfitting, improve the transparency of the models, and to save computational resources. Although there is a plethora of evolutionary approaches to extract models for classification and regression, the presented approach is one of the first applied to survival analysis. The approach is evaluated on several artificial datasets and one medical dataset. It is shown that the approach is capable of producing accurate models, even for problems that violate some of the assumptions made by classical approaches.
SN  - 0303-2647
UR  - http://dx.doi.org/10.1016/j.biosystems.2006.03.002
UR  - http://www.ncbi.nlm.nih.gov/pubmed/16762491
ID  - Setzkorn2007
ER  - 
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