Skip to main content

Genetic Programming Symbolic Classification: A Study

Part of the Genetic and Evolutionary Computation book series (GEVO)

Abstract

While Symbolic Regression (SR) is a well-known offshoot of Genetic Programming, Symbolic Classification (SC), by comparison, has received only meager attention. Clearly, regression is only half of the solution. Classification also plays an important role in any well rounded predictive analysis tool kit. In several recent papers, SR algorithms are developed which move SR into the ranks of extreme accuracy. In an additional set of papers algorithms are developed designed to push SC to the level of basic classification accuracy competitive with existing commercially available classification tools. This paper is a simple study of four proposed SC algorithms and five well-known commercially available classification algorithms to determine just where SC now ranks in competitive comparison. The four SC algorithms are: simple genetic programming using argmax referred to herein as (AMAXSC); the M2GP algorithm; the MDC algorithm, and Linear Discriminant Analysis (LDA). The five commercially available classification algorithms are available in the KNIME system, and are as follows: Decision Tree Learner (DTL); Gradient Boosted Trees Learner (GBTL); Multiple Layer Perceptron Learner (MLP); Random Forest Learner (RFL); and Tree Ensemble Learner (TEL). A set of ten artificial classification problems are constructed with no noise. The simple formulas for these ten artificial problems are listed herein. The problems vary from linear to nonlinear multimodal and from 25 to 1000 columns. All problems have 5000 training points and a separate 5000 testing points. The scores, on the out of sample testing data, for each of the nine classification algorithms are published herein.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-90512-9_3
  • Chapter length: 16 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-90512-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.99
Price excludes VAT (USA)
Hardcover Book
USD   109.99
Price excludes VAT (USA)

References

  1. Ingalalli, Vijay, Silva, Sara, Castelli, Mauro, Vanneschi, Leonardo 2014. A Multi-dimensional Genetic Programming Approach for Multi-class Classification Problems. Euro GP 2014 Springer, pp. 48–60.

    Google Scholar 

  2. Korns, Michael F. 2013. Extreme Accuracy in Symbolic Regression. Genetic Programming Theory and Practice XI. Springer, New York, NY, pp. 1–30.

    Google Scholar 

  3. Koza, John R. 1992. Genetic Programming: On the Programming of Computers by means of Natural Selection. The MIT Press. Cambridge, Massachusetts.

    Google Scholar 

  4. Korns, Michael F. 2012. A Baseline Symbolic Regression Algorithm. Genetic Programming Theory and Practice X. Springer, New York, NY.

    Google Scholar 

  5. Keijzer, Maarten. 2003. Improving Symbolic Regression with Interval Arithmetic and Linear Scaling. European Conference on Genetic Programming. Springer, Berlin, pp. 275–299.

    Google Scholar 

  6. Billard, Billard., Diday, Edwin. 2003. Symbolic Regression Analysis. Springer. New York, NY.

    MATH  Google Scholar 

  7. Korns, Michael F. 2015. Extremely Accurate Symbolic Regression for Large Feature Problems. Genetic Programming Theory and Practice XII. Springer, New York, NY, pp. 109–131.

    Google Scholar 

  8. Korns, Michael F. 2016. Highly Accurate Symbolic Regression for Noisy Training Data. Genetic Programming Theory and Practice XIII. Springer, New York, NY, pp. 91–115.

    CrossRef  Google Scholar 

  9. Korns, Michael F. 2018. An Evolutionary Algorithm for Big Data Multi-class Classification Problems. In William Tozier and Brian W. Goldman and Bill Worzel and Rick Riolo editors, Genetic Programming Theory and Practice XIV, Ann Arbor, USA, 2016. www.cs.bham. ac.uk/∼wbl/biblio/gp-html/MichaelKorns.html.

  10. Munoz, Louis, Silva, Sara, M. Castelli, Trujillo 2014. M 3 GP Multiclass Classification with GP. Proceedings Euro GP 2015 Springer, pp. 78–91.

    Google Scholar 

  11. Fisher, R. A. 1936. The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics 7 (2) 179–188.

    CrossRef  Google Scholar 

  12. Friedman, J. H. 1989. Regularized Discriminant Analysis. Journal of American Statistical Association 84 (405) 165–175.

    MathSciNet  CrossRef  Google Scholar 

  13. McLachan, Geoffrey, J. 2004. Discriminant Analysis and Statistical Pattern Recognition. Wiley. New York, NY.

    Google Scholar 

  14. Korns, Michael F., 2017. Evolutionary Linear Discriminant Analysis for Multiclass Classification Problems. GECCO Conference Proceedings ’17, July 15–19, Berlin, Germany. ACM Press, New York (2017), pp. 233–234.

    Google Scholar 

  15. Michael R. Berthold, Nicolas Cebron, Fabian Dill, Thomas R. Gabriel, Tobias Kötter, Thorsten Meinl, Peter Ohl, Christoph Sieb, Kilian Thiel, and Bernd Wiswedel, 2007. KNIME: The Konstanz Information Miner. ACM SIGKDD Explorations Newsletter. ACM Press, New York (2009), pp. 26–31.

    Google Scholar 

Download references

Acknowledgements

Our thanks to: Thomas May from Lantern Credit for assisting with the KNIME Learner training/scoring on all ten artificial classification problems.

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Korns, M.F. (2018). Genetic Programming Symbolic Classification: A Study. In: Banzhaf, W., Olson, R., Tozier, W., Riolo, R. (eds) Genetic Programming Theory and Practice XV. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-319-90512-9_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-90512-9_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90511-2

  • Online ISBN: 978-3-319-90512-9

  • eBook Packages: Computer ScienceComputer Science (R0)