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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Poli, R., Langdon, W., McPhee, N.: A Field Guide to Genetic Programming (2008), Published via http://lulu.com and freely available at, http://www.gp-field-guide.org.uk (With contributions by Koza, J.R.)
Espejo, P.G., Ventura, S., Herrera, F.: A survey on the application of genetic programming to classification. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 40(2), 121–144 (2010)
Petrović, N.I., Crnojević, V.S.: Impulse noise detection based on robust statistics and genetic programming. In: Blanc-Talon, J., Philips, W., Popescu, D.C., Scheunders, P. (eds.) ACIVS 2005. LNCS, vol. 3708, pp. 643–649. Springer, Heidelberg (2005)
Li, J., Li, X., Yao, X.: Cost-sensitive classification with genetic programming. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, vol. 3, pp. 2114–2121 (2005)
Loveard, T., Ciesielski, V.: Representing classification problems in genetic programming. In: Proceedings of the Congress on Evolutionary Computation, vol. 2, pp. 1070–1077 (2001)
Zhang, M., Smart, W.: Multiclass object classification using genetic programming. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 369–378. Springer, Heidelberg (2004)
Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
Fortin, F.-A., Rainville, F.-M.D., Gardner, M.-A., Parizeau, M., Gagné, C.: DEAP: Evolutionary algorithms made easy. Journal of Machine Learning Research 13, 2171–2175 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de Jong, J., Neshatian, K. (2013). Binary Classification Using Genetic Programming: Evolving Discriminant Functions with Dynamic Thresholds. In: Li, J., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7867. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40319-4_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-40319-4_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40318-7
Online ISBN: 978-3-642-40319-4
eBook Packages: Computer ScienceComputer Science (R0)