Convex Hull-Based Multi-objective Genetic Programming for Maximizing Receiver Operating Characteristic Performance

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  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 Receiver Operating Characteristic
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2015",
  volume =       "19",
  number =       "2",
  pages =        "188--200",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming,
                 Classification, ROC Convex Hull, Evolutionary
                 Multi-objective Algorithm, Memetic Algorithm",
  DOI =          "doi:10.1109/TEVC.2014.2305671",
  ISSN =         "1089-778X",
  size =         "13 pages",
  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
  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