Evolving Diverse Ensembles using Genetic Programming for Classification with Unbalanced Data

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

@Article{Bhowan:2012:ieeeTEC,
  author =       "Urvesh Bhowan and Mark Johnston and Mengjie Zhang and 
                 Xin Yao",
  title =        "Evolving Diverse Ensembles using Genetic Programming
                 for Classification with Unbalanced Data",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2013",
  volume =       "17",
  number =       "3",
  pages =        "368--386",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6198882",
  DOI =          "doi:10.1109/TEVC.2012.2199119",
  size =         "19 pages",
  abstract =     "In classification, machine learning algorithms can
                 suffer a performance bias when data sets are
                 unbalanced. Data sets are unbalanced when at least one
                 class is represented by only a small number of training
                 examples (called the minority class) while the other
                 class(es) make up the majority. In this scenario,
                 classifiers can have good accuracy on the majority
                 class but very poor accuracy on the minority class(es).
                 This paper proposes a Multi-objective Genetic
                 Programming (MOGP) approach to evolving accurate and
                 diverse ensembles of genetic program classifiers with
                 good performance on both the minority and majority
                 classes. The evolved ensembles comprise of nondominated
                 solutions in the population where individual members
                 vote on class membership. This paper evaluates the
                 effectiveness of two popular Pareto-based fitness
                 strategies in the MOGP algorithm (SPEA2 and NSGAII),
                 and investigates techniques to encourage diversity
                 between solutions in the evolved ensembles.
                 Experimental results on six (binary) class imbalance
                 problems show that the evolved ensembles outperform
                 their individual members, as well as single-predictor
                 methods such as canonical GP, Naive Bayes and Support
                 Vector Machines, on highly unbalanced tasks. This
                 highlights the importance of developing an effective
                 fitness evaluation strategy in the underlying MOGP
                 algorithm to evolve good ensemble members.",
  ISSN =         "1089-778X",
  notes =        "NSGA-II, SPEA2

                 Also known as \cite{6198882}",
}

Genetic Programming entries for Urvesh Bhowan Mark Johnston Mengjie Zhang Xin Yao

Citations