Skip to main content

Novel Decision Forest Building Techniques by Utilising Correlation Coefficient Methods

  • 697 Accesses

Part of the Communications in Computer and Information Science book series (CCIS,volume 1600)


Decision Forests have attracted the academic community’s interest mainly due to their simplicity and transparency. This paper proposes two novel decision forest building techniques, called Maximal Information Coefficient Forest (MICF) and Pearson’s Correlation Coefficient Forest (PCCF). The proposed new algorithms use Pearson’s Correlation Coefficient (PCC) and Maximal Information Coefficient (MIC) as extra measures of the classification capacity score of each feature. Using those approaches, we improve the picking of the most convenient feature at each splitting node, the feature with the greatest Gain Ratio. We conduct experiments on 12 datasets that are available in the publicly accessible UCI machine learning repository. Our experimental results indicate that the proposed methods have the best average ensemble accuracy rank of 1.3 (for MICF) and 3.0 (for PCCF), compared to their closest competitor, Random Forest (RF), which has an average rank of 4.3. Additionally, the results from Friedman and Bonferroni-Dunn tests indicate statistically significant improvement.


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

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions


  1. 1.


  1. Adnan, N.: Decision tree and decision forest algorithms: on improving accuracy, efficiency and knowledge discovery (2017)

    Google Scholar 

  2. Bernard, S., Heutte, L., Adam, S.: Forest-rk: a new random forest induction method. In: ICIC (2008)

    Google Scholar 

  3. Breiman, L.: Bagging predictors. Mach. Learn. 24, 123–140 (2004)

    MATH  Google Scholar 

  4. Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2004)

    Article  Google Scholar 

  5. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees (1983)

    Google Scholar 

  6. Delgado, M.F., Cernadas, E., Barro, S., Amorim, D.G.: Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res. 15, 3133–3181 (2014)

    MathSciNet  MATH  Google Scholar 

  7. Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

  8. Drousiotis, E., Pentaliotis, P., Shi, L., Cristea, A.I.: Capturing fairness and uncertainty in student dropout prediction – a comparison study. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds.) AIED 2021. LNCS (LNAI), vol. 12749, pp. 139–144. Springer, Cham (2021).

    Chapter  Google Scholar 

  9. Drousiotis, E., Shi, L., Maskell, S.: Early predictor for student success based on behavioural and demographical indicators. In: Cristea, A.I., Troussas, C. (eds.) ITS 2021. LNCS, vol. 12677, pp. 161–172. Springer, Cham (2021).

    Chapter  Google Scholar 

  10. Dunn, O.J.: Multiple comparisons among means. J. Am. Stat. Assoc. 56(293), 52–64 (1961)

    Article  MathSciNet  Google Scholar 

  11. Friedman, M.: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32(200), 675–701 (1937)

    Article  Google Scholar 

  12. Friedman, M.: A comparison of alternative tests of significance for the problem of \$m\$ rankings. Ann. Math. Stat. 11, 86–92 (1940)

    Article  MathSciNet  Google Scholar 

  13. Guo, Z., Yu, B., Hao, M., Wang, W., Jiang, Y., Zong, F.: A novel hybrid method for flight departure delay prediction using random forest regression and maximal information coefficient (2021)

    Google Scholar 

  14. Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20, 832–844 (1998)

    Article  Google Scholar 

  15. Iman, R.L., Davenport, J.M.: Approximations of the critical region of the Fbietkan statistic. Commun. Stat.-Theory Methods 9, 571–595 (1980)

    Article  Google Scholar 

  16. Liu, S., Hu, T.: Parallel random forest algorithm optimization based on maximal information coefficient. In: 2018 IEEE 9th International Conference on Software Engineering and Service Science, pp. 1083–1087 (2018)

    Google Scholar 

  17. Maudes, J., Rodríguez, J.J., García-Osorio, C., García-Pedrajas, N.: Random feature weights for decision tree ensemble construction. Inf. Fusion 13(1), 20–30 (2012)

    Article  Google Scholar 

  18. Murthy, S.K.: Automatic construction of decision trees from data: a multi-disciplinary survey. Data Mining Knowl. Disc. 2, 345–389 (2004)

    Article  Google Scholar 

  19. Nasridinov, A., Ihm, S., Park, Y.H.: A decision tree-based classification model for crime prediction. In: ITCS (2013)

    Google Scholar 

  20. Podgorelec, V., Kokol, P., Stiglic, B., Rozman, I.: Decision trees: an overview and their use in medicine. J. Med. Syst. 26, 445–463 (2004)

    Article  Google Scholar 

  21. Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81–106 (1986)

    Google Scholar 

  22. Salzberg, S., Murthy, K.: On growing better decision trees from data (1996)

    Google Scholar 

  23. Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Education India (2016)

    Google Scholar 

  24. Zeleznikow, J.: Using web-based legal decision support systems to improve access to justice. Inf. Commun. Technol. Law 11, 15–33 (2002)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Efthyvoulos Drousiotis or Lei Shi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Drousiotis, E., Shi, L., Spirakis, P.G., Maskell, S. (2022). Novel Decision Forest Building Techniques by Utilising Correlation Coefficient Methods. In: Iliadis, L., Jayne, C., Tefas, A., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2022. Communications in Computer and Information Science, vol 1600. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08222-1

  • Online ISBN: 978-3-031-08223-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics