HEAD-DT: Fitness Function Analysis

  • Rodrigo C. BarrosEmail author
  • André C. P. L. F. de Carvalho
  • Alex A. Freitas
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)


In Chap.  4, more specifically in Sect.  4.4, we saw that the definition of a fitness function for the scenario in which HEAD-DT evolves a decision-tree algorithm from multiple data sets is an interesting and relevant problem. In the experiments presented in Chap.  5, Sect.  5.2, we employed a simple average over the F-Measure obtained in the data sets that belong to the meta-training set. As previously observed, when evolving an algorithm from multiple data sets, each individual of HEAD-DT has to be executed over each data set in the meta-training set. Hence, instead of obtaining a single value of predictive performance, each individual scores a set of values that have to be eventually combined into a single measure. In this chapter, we analyse in more detail the impact of different strategies to be used as fitness function during the evolutionary cycle of HEAD-DT. We divide the experimental scheme into two distinct scenarios: (i) evolving a decision-tree induction algorithm from multiple balanced data sets; and (ii) evolving a decision-tree induction algorithm from multiple imbalanced data sets. In each of these scenarios, we analyse the difference in performance of well-known performance measures such as accuracy, F-Measure, AUC, recall, and also a lesser-known criterion, namely the relative accuracy improvement. In addition, we analyse different schemes of aggregation, such as simple average, median, and harmonic mean.


Fitness functions Performance measures Evaluation schemes 


  1. 1.
    T. Fawcett, An introduction to ROC analysis. Pattern Recognit. Lett. 27(8), 861–874 (2006)CrossRefMathSciNetGoogle Scholar
  2. 2.
    C. Ferri, J. Hernández-Orallo, R. Modroiu, An experimental comparison of performance measures for classification. Pattern Recognit. Lett. 30(1), 27–38 (2009)CrossRefGoogle Scholar
  3. 3.
    B. Hanczar et al., Small-sample precision of ROC-related estimates. Bioinformatics 26(6), 822–830 (2010)CrossRefGoogle Scholar
  4. 4.
    D.J. Hand, Measuring classifier performance: a coherent alternative to the area under the ROC curve. Mach. Learn. 77(1), 103–123 (2009)CrossRefGoogle Scholar
  5. 5.
    J.M. Lobo, A. Jiménez-Valverde, R. Real, AUC: a misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17(2), 145–151 (2008)CrossRefGoogle Scholar
  6. 6.
    S.J. Mason, N.E. Graham, Areas beneath the relative operating characteristics (roc) and relative operating levels (rol) curves: statistical significance and interpretation. Q. J. R. Meteorol. Soc. 128(584), 2145–2166 (2002)CrossRefGoogle Scholar
  7. 7.
    G.L. Pappa, Automatically evolving rule induction algorithms with grammar-based genetic programming, Ph.D. thesis. University of Kent at Canterbury (2007)Google Scholar
  8. 8.
    D. Powers, Evaluation: From precision, recall and f-measure to ROC, informedness, markedness and correlation. J. Mach. Learn. Technol. 2(1), 37–63 (2011)MathSciNetGoogle Scholar

Copyright information

© The Author(s) 2015

Authors and Affiliations

  • Rodrigo C. Barros
    • 1
    Email author
  • André C. P. L. F. de Carvalho
    • 2
  • Alex A. Freitas
    • 3
  1. 1.Faculdade de InformáticaPontifícia Universidade Católica do Rio Grande do SulPorto AlegreBrazil
  2. 2.Instituto de Ciências Matemáticas e de ComputaçãoUniversidade de São PauloSão CarlosBrazil
  3. 3.School of ComputingUniversity of KentCanterburyUK

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