Binary Classification Using Genetic Programming: Evolving Discriminant Functions with Dynamic Thresholds

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

@InProceedings{conf/pakdd/JongN13,
  author =       "Jill {de Jong} and Kourosh Neshatian",
  title =        "Binary Classification Using Genetic Programming:
                 Evolving Discriminant Functions with Dynamic
                 Thresholds",
  booktitle =    "Trends and Applications in Knowledge Discovery and
                 Data Mining",
  editor =       "Jiuyong Li and Longbing Cao and Can Wang and 
                 Kay Chen Tan and Bo Liu and Jian Pei and Vincent S. Tseng",
  year =         "2013",
  volume =       "7867",
  series =       "Lecture Notes in Computer Science",
  pages =        "464--474",
  address =      "Gold Coast, Australia",
  month =        apr # " 14-17",
  publisher =    "Springer",
  note =         "Revised Selected Papers",
  keywords =     "genetic algorithms, genetic programming",
  bibdate =      "2013-08-27",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/pakdd/pakdd2013-w.html#JongN13",
  isbn13 =       "978-3-642-40318-7",
  URL =          "http://dx.doi.org/10.1007/978-3-642-40319-4",
  DOI =          "doi:10.1007/978-3-642-40319-4_40",
  size =         "11 pages",
  abstract =     "Binary classification is the problem of predicting
                 which of two classes an input vector belongs to. This
                 problem can be solved by using genetic programming to
                 evolve discriminant functions which have a threshold
                 output value that distinguishes between the two
                 classes. The standard approach is to have a static
                 threshold value of zero that is fixed throughout the
                 evolution process. Items with a positive function
                 output value are identified as one class and items with
                 a negative function output value as the other class. We
                 investigate a different approach where an optimum
                 threshold is dynamically determined for each candidate
                 function during the fitness evaluation. The optimum
                 threshold is the one that achieves the lowest
                 misclassification cost. It has an associated class
                 translation rule for output values either side of the
                 threshold value. The two approaches have been compared
                 experimentally using four different datasets. Results
                 suggest the dynamic threshold approach consistently
                 achieves higher performance levels than the standard
                 approach after equal numbers of fitness calls.",
  notes =        "PAKDD 2013 International Workshops: DMApps, DANTH,
                 QIMIE, BDM, CDA, CloudSD, 2013",
}

Genetic Programming entries for Jill de Jong Kourosh Neshatian

Citations