Multiobjective Optimization of Classifiers by Means of 3D Convex-Hull-Based Evolutionary Algorithms

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

@Article{Zhao:2016:IS,
  author =       "Jiaqi Zhao and Vitor Basto Fernandes and 
                 Licheng Jiao and Iryna Yevseyeva and Asep Maulana and Rui Li and 
                 Thomas Back and Ke Tang and Michael T. M. Emmerich",
  title =        "Multiobjective Optimization of Classifiers by Means of
                 {3D} Convex-Hull-Based Evolutionary Algorithms",
  journal =      "Information Sciences",
  year =         "2016",
  volume =       "367-368",
  pages =        "80--104",
  month =        "1 " # nov,
  keywords =     "genetic algorithms, genetic programming, Convex hull,
                 Classification, Evolutionary multiobjective
                 optimization, Parsimony, ROC analysis, Anti-spam
                 filters",
  ISSN =         "0020-0255",
  DOI =          "doi:10.1016/j.ins.2016.05.026",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0020025516303504",
  abstract =     "The receiver operating characteristic (ROC) and
                 detection error tradeoff (DET) curves are frequently
                 used in the machine learning community to analyse the
                 performance of binary classifiers. Recently, the
                 convex-hull-based multiobjective genetic programming
                 algorithm was proposed and successfully applied to
                 maximize the convex hull area for binary classification
                 problems by minimizing false positive rate and
                 maximizing true positive rate at the same time using
                 indicator-based evolutionary algorithms. The area under
                 the ROC curve was used for the performance assessment
                 and to guide the search. Here we extend this research
                 and propose two major advancements: Firstly we
                 formulate the algorithm in detection error tradeoff
                 space, minimizing false positives and false negatives,
                 with the advantage that misclassification cost tradeoff
                 can be assessed directly. Secondly, we add complexity
                 as an objective function, which gives rise to a 3D
                 objective space (as opposed to a 2D previous ROC
                 space). A domain specific performance indicator for 3D
                 Pareto front approximations, the volume above DET
                 surface, is introduced, and used to guide the
                 indicator-based evolutionary algorithm to find optimal
                 approximation sets. We assess the performance of the
                 new algorithm on designed theoretical problems with
                 different geometries of Pareto fronts and DET surfaces,
                 and two application-oriented benchmarks: (1) Designing
                 spam filters with low numbers of false rejects, false
                 accepts, and low computational cost using rule
                 ensembles, and (2) finding sparse neural networks for
                 binary classification of test data from the UCI machine
                 learning benchmark. The results show a high performance
                 of the new algorithm as compared to conventional
                 methods for multicriteria optimization.",
  notes =        "Replaces \cite{oai:arXiv.org:1412.5710}?",
}

Genetic Programming entries for Jiaqi Zhao Vitor Basto-Fernandes Licheng Jiao Iryna Yevseyeva Asep Maulana Rui Li Thomas Back Ke Tang Michael Emmerich

Citations