One-class Classification for Anomaly Detection with Kernel Density Estimation and Genetic Programming

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

@InProceedings{Cao:2016:EuroGP,
  author =       "Van Loi Cao and Miguel Nicolau and James McDermott",
  title =        "One-class Classification for Anomaly Detection with
                 Kernel Density Estimation and Genetic Programming",
  booktitle =    "EuroGP 2016: Proceedings of the 19th European
                 Conference on Genetic Programming",
  year =         "2016",
  month =        "30 " # mar # "--1 " # apr,
  editor =       "Malcolm I. Heywood and James McDermott and 
                 Mauro Castelli and Ernesto Costa and Kevin Sim",
  series =       "LNCS",
  volume =       "9594",
  publisher =    "Springer Verlag",
  address =      "Porto, Portugal",
  pages =        "3--18",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, Anomaly
                 detection, One-class classification, Kernel Density
                 Estimation",
  isbn13 =       "978-3-319-30668-1",
  DOI =          "doi:10.1007/978-3-319-30668-1_1",
  abstract =     "A novel approach is proposed for fast anomaly
                 detection by one-class classification. Standard kernel
                 density estimation is first used to obtain an estimate
                 of the input probability density function, based on the
                 one-class input data. This can be used for anomaly
                 detection: query points are classed as anomalies if
                 their density is below some threshold. The disadvantage
                 is that kernel density estimation is lazy, that is the
                 bulk of the computation is performed at query time. For
                 large datasets it can be slow. Therefore it is proposed
                 to approximate the density function using genetic
                 programming symbolic regression, before imposing the
                 threshold. The runtime of the resulting genetic
                 programming trees does not depend on the size of the
                 training data. The method is tested on datasets
                 including in the domain of network security. Results
                 show that the genetic programming approximation is
                 generally very good, and hence classification accuracy
                 approaches or equals that when using kernel density
                 estimation to carry out one-class classification
                 directly. Results are also generally superior to
                 another standard approach, one-class support vector
                 machines.",
  notes =        "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in
                 conjunction with EvoCOP2016, EvoMusArt2016 and
                 EvoApplications2016",
}

Genetic Programming entries for Van Loi Cao Miguel Nicolau James McDermott

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