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

  • Jill de Jong
  • Kourosh Neshatian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7867)

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.

Keywords

Genetic Programming Input Vector Discriminant Function Class Label Training Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jill de Jong
    • 1
  • Kourosh Neshatian
    • 1
  1. 1.Department of Computer Science and Software EngineeringUniversity of CanterburyChristchurchNew Zealand

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