Convex Hull-Based Multi-objective Genetic Programming for Maximizing ROC Performance
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- @Misc{DBLP:journals/corr/abs-1303-3145,
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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 ROC Performance",
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year = "2013",
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volume = "abs/1303.3145",
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month = "15 " # mar,
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note = "v2",
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keywords = "genetic algorithms, genetic programming",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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URL = "http://arxiv.org/abs/1303.3145",
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size = "23 pages",
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abstract = "ROC is usually used to analyse the performance of
classifiers in data mining. ROC convex hull (ROCCH) is
the least convex major-ant (LCM) of the empirical ROC
curve, and covers potential optima for the given set of
classifiers. Generally, ROC performance maximisation
could be considered to maximise the ROCCH, which also
means to maximize the true positive rate (tpr) and
minimise the false positive rate (fpr) for each
classifier in the ROC space. However, tpr and fpr are
conflicting with each other in the ROCCH optimisation
process. Though ROCCH maximisation problem seems like a
multi-objective optimisation problem (MOP), the special
characters make it different from traditional MOP. In
this work, we will discuss the difference between them
and propose convex hull-based multi-objective genetic
programming (CH-MOGP) to solve ROCCH maximization
problems. Convex hull-based sort is an indicator based
selection scheme that aims to maximize the area under
convex hull, which serves as a unary indicator for the
performance of a set of points. A selection procedure
is described that can be efficiently implemented and
follows similar design principles than classical
hyper-volume based optimization algorithms. It is
suggested that by using a tailored indicator-based
selection scheme CH-MOGP gets more efficient for ROC
convex hull approximation than algorithms which compute
all Pareto optimal points. To test our hypothesis we
compare the new CH-MOGP to MOGP with classical
selection schemes, including NSGA-II, MOEA/D) and
SMS-EMOA. Meanwhile, CH-MOGP is also compared with
traditional machine learning algorithms such as C4.5,
Naive Bayes and PRIE. Experimental results based on 22
well-known UCI data sets show that CH-MOGP outperforms
significantly traditional EMOAs.",
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notes = "See \cite{Wang:2014:ieeeEC}",
- }
Genetic Programming entries for
Pu Wang
Michael Emmerich
Rui Li
Ke Tang
Thomas Back
Xin Yao
Citations