Multiobjective genetic programming for maximizing ROC performance

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Pu Wang and Ke Tang and Thomas Weise and 
                 E. P. K. Tsang and Xin Yao",
  title =        "Multiobjective genetic programming for maximizing
                 {ROC} performance",
  journal =      "Neurocomputing",
  year =         "2014",
  volume =       "125",
  pages =        "102--118",
  keywords =     "genetic algorithms, genetic programming,
                 Classification, ROC analysis, AUC, ROCCH, Evolutionary
                 multiobjective algorithm, Memetic algorithm, Decision
  ISSN =         "0925-2312",
  URL =          "",
  DOI =          "doi:10.1016/j.neucom.2012.06.054",
  URL =          "",
  size =         "17 pages",
  abstract =     "In binary classification problems, receiver operating
                 characteristic (ROC) graphs are commonly used for
                 visualising, organising and selecting classifiers based
                 on their performances. An important issue in the ROC
                 literature is to obtain the ROC convex hull (ROCCH)
                 that covers potentially optima for a given set of
                 classifiers [1]. Maximising the ROCCH means to maximise
                 the true positive rate (tpr) and minimise the false
                 positive rate (fpr) for every classifier in ROC space,
                 while tpr and fpr are conflicting with each other. In
                 this paper, we propose multiobjective genetic
                 programming (MOGP) to obtain a group of nondominated
                 classifiers, with which the maximum ROCCH can be
                 achieved. Four different multiobjective frameworks,
                 including Nondominated Sorting Genetic Algorithm II
                 (NSGA-II), Multiobjective Evolutionary Algorithms Based
                 on Decomposition (MOEA/D), Multiobjective selection
                 based on dominated hypervolume (SMS-EMOA), and
                 Approximation-Guided Evolutionary Multi-Objective
                 (AG-EMOA) are adopted into GP, because all of them are
                 successfully applied into many problems and have their
                 own characters. To improve the performance of each
                 individual in GP, we further propose a memetic approach
                 into GP by defining two local search strategies
                 specifically designed for classification problems.
                 Experimental results based on 27 well-known UCI data
                 sets show that MOGP performs significantly better than
                 single objective algorithms such as FGP, GGP, EGP, and
                 MGP, and other traditional machine learning algorithms
                 such as C4.5, Naive Bayes, and PRIE. The experiments
                 also demonstrate the efficacy of the local search
                 operator in the MOGP framework.",
  notes =        "Selected papers from the 9th International Symposium
                 of Neural Networks, July 2012. Advances in Neural
                 Network Research and Applications. Advances in
                 Bio-Inspired Computing: Techniques and Applications",

Genetic Programming entries for Pu Wang Ke Tang Thomas Weise Edward P K Tsang Xin Yao