Convex Hull-Based Multi-objective Genetic Programming for Maximizing Receiver Operating Characteristic Performance
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Wang:2014:ieeeEC,
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author = "Pu Wang and Michael Emmerich and Rui Li and
Ke Tang and Thomas Baeck and Xin Yao",
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title = "Convex Hull-Based Multi-objective Genetic Programming
for Maximizing Receiver Operating Characteristic
Performance",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2015",
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volume = "19",
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number = "2",
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pages = "188--200",
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month = apr,
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keywords = "genetic algorithms, genetic programming,
Classification, ROC Convex Hull, Evolutionary
Multi-objective Algorithm, Memetic Algorithm",
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DOI = "doi:10.1109/TEVC.2014.2305671",
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ISSN = "1089-778X",
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size = "13 pages",
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abstract = "The receiver operating characteristic (ROC) is
commonly used to analyse the performance of classifiers
in data mining. An important topic in ROC analysis is
the ROC convex hull (ROCCH), which is the least convex
majorant (LCM) of the empirical ROC curve and covers
potential optima for a given set of classifiers. ROCCH
maximisation problems have been taken as
multi-objective optimisation problem (MOPs) in some
previous work. However, the special characteristics of
ROCCH maximisation problem makes it different from
traditional MOPs. In this work, the difference will be
discussed in detail and a new convex hull-based
multi-objective genetic programming (CH-MOGP) is
proposed to solve ROCCH maximisation problems.
Specifically, convex hull-based without redundancy
sorting (CWR-sorting) is introduced, which is an
indicator based selection scheme that aims to maximise
the area under the convex hull. A novel selection
procedure is also proposed based on the proposed
sorting scheme. It is suggested that by using a
tailored indicator-based selection, CH-MOGP becomes
more efficient for ROC convex hull approximation than
algorithms which compute all Pareto optimal points.
Empirical studies are conducted to compare CH-MOGP to
both existing machine learning approaches and
multi-objective genetic programming (MOGP) methods with
classical selection schemes. Experimental results show
that CH-MOGP outperforms the other approaches
significantly.",
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notes = "See \cite{DBLP:journals/corr/abs-1303-3145} Also known
as \cite{6762993}",
- }
Genetic Programming entries for
Pu Wang
Michael Emmerich
Rui Li
Ke Tang
Thomas Back
Xin Yao
Citations