Skip to main content

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

  • 1795 Accesses

Abstract

We presented in this book an approach for the automatic design of decision-tree induction algorithms, namely HEAD-DT (Hyper-Heuristic Evolutionary Algorithm for Automatically Designing Decision-Tree Induction Algorithms).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 59.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. R.C. Barros et al., A bottom-up oblique decision tree induction algorithm, in 11th International Conference on Intelligent Systems Design and Applications. pp. 450–456 (2011)

    Google Scholar 

  2. R.C. Barros et al., A framework for bottom-up induction of decision trees, in Neurocomputing (in press 2013)

    Google Scholar 

  3. M.P. Basgalupp et al., A beam-search based decision-tree induction algorithm, in Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques. IGI-Global (2011)

    Google Scholar 

  4. P. Brazdil et al., Metalearning—Applications to Data Mining. Cognitive Technologies (Springer, Berlin, 2009), pp. I-X, 1–176. ISBN: 978-3-540-73262-4

    Google Scholar 

  5. L. Breiman et al., Classification and Regression Trees (Wadsworth, Belmont, 1984)

    MATH  Google Scholar 

  6. A.E. Eiben, S.K. Smit, Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm Evol. Comput. 1(1), 19–31 (2011)

    Article  Google Scholar 

  7. A. Frank, A. Asuncion. UCI Machine Learning Repository (2010)

    Google Scholar 

  8. A.A. Freitas, A critical review of multi-objective optimization in data mining: a position paper. SIGKDD Explor. Newsl. 6(2), 77–86 (2004). ISSN: 1931–0145

    Article  MathSciNet  Google Scholar 

  9. O. Kramer, Self-Adaptive Crossover, Self-Adaptive Heuristics for Evolutionary Computation., Vol. 147. Studies in Computational Intelligence (Springer, Berlin, 2008)

    Google Scholar 

  10. G. Landeweerd et al., Binary tree versus single level tree classification of white blood cells. Pattern Recognit. 16(6), 571–577 (1983)

    Article  Google Scholar 

  11. J.R. Quinlan, C4.5: Programs for Machine Learning (Morgan Kaufmann, San Francisco, 1993). ISBN: 1-55860-238-0

    Google Scholar 

  12. M. Souto et al., Clustering cancer gene expression data: a comparative study. BMC Bioinform. 9(1), 497 (2008)

    Article  Google Scholar 

  13. C.T. Yildiz, E. Alpaydin, Omnivariate decision trees. IEEE Trans. Neural Netw. 12(6), 1539–1546 (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rodrigo C. Barros .

Rights and permissions

Reprints and permissions

Copyright information

© 2015 The Author(s)

About this chapter

Cite this chapter

Barros, R.C., de Carvalho, A.C.P.L.F., Freitas, A.A. (2015). Conclusions. In: Automatic Design of Decision-Tree Induction Algorithms. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-14231-9_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-14231-9_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14230-2

  • Online ISBN: 978-3-319-14231-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics