Convex Hull-Based Multi-objective Genetic Programming for Maximizing ROC Performance

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@Misc{DBLP:journals/corr/abs-1303-3145,
  author =       "Pu Wang and Michael Emmerich and Rui Li and 
                 Ke Tang and Thomas Baeck and Xin Yao",
  title =        "Convex Hull-Based Multi-objective Genetic Programming
                 for Maximizing ROC Performance",
  year =         "2013",
  volume =       "abs/1303.3145",
  month =        "15 " # mar,
  note =         "v2",
  keywords =     "genetic algorithms, genetic programming",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  URL =          "http://arxiv.org/abs/1303.3145",
  size =         "23 pages",
  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.",
  notes =        "See \cite{Wang:2014:ieeeEC}",
}

Genetic Programming entries for Pu Wang Michael Emmerich Rui Li Ke Tang Thomas Back Xin Yao

Citations