Multi-class pattern classification using single, multi-dimensional feature-space feature extraction evolved by multi-objective genetic programming and its application to network intrusion detection

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

@Article{Badran:2011:GPEM,
  author =       "Khaled Badran and Peter Rockett",
  title =        "Multi-class pattern classification using single,
                 multi-dimensional feature-space feature extraction
                 evolved by multi-objective genetic programming and its
                 application to network intrusion detection",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2012",
  volume =       "13",
  number =       "1",
  pages =        "33--63",
  month =        mar,
  note =         "Special Section on Evolutionary Algorithms for Data
                 Mining",
  keywords =     "genetic algorithms, genetic programming, Multi-class
                 pattern classification, Feature extraction, Feature
                 selection, Multi-objective genetic programming",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-011-9143-4",
  size =         "31 pages",
  abstract =     "In this paper we investigate using multi-objective
                 genetic programming to evolve a feature extraction
                 stage for multiple-class classifiers. We find mappings
                 which transform the input space into a new,
                 multi-dimensional decision space to increase the
                 discrimination between all classes; the number of
                 dimensions of this decision space is optimised as part
                 of the evolutionary process. A simple and fast
                 multi-class classifier is then implemented in this
                 multi-dimensional decision space. Mapping to a single
                 decision space has significant computational advantages
                 compared to k -class-to-2-class decompositions; a key
                 design requirement in this work has been the ability to
                 incorporate changing priors and/or costs associated
                 with mislabelling without retraining. We have employed
                 multi-objective optimization in a Pareto framework
                 incorporating solution complexity as an independent
                 objective to be minimised in addition to the main
                 objective of the misclassification error. We thus give
                 preference to simpler solutions which tend to
                 generalise well on unseen data, in accordance with
                 Occam's Razor. We obtain classification results on a
                 series of benchmark problems which are essentially
                 identical to previous, more complex decomposition
                 approaches. Our solutions are much simpler and
                 computationally attractive as well as able to readily
                 incorporate changing priors/costs. In addition, we have
                 also applied our approach to the KDD-99 intrusion
                 detection dataset and obtained results which are highly
                 competitive with the KDD-99 Cup winner but with a
                 significantly simpler classification framework.",
  affiliation =  "Vision and Information Engineering Research Group,
                 Department of Electronic and Electrical Engineering,
                 The University of Sheffield, Mappin Street, Sheffield,
                 S1 3D UK",
}

Genetic Programming entries for Khaled M S Badran Peter I Rockett

Citations