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Novel Decision Forest Building Techniques by Utilising Correlation Coefficient Methods

Drousiotis, Efthyvoulos and Shi, Lei and Spirakis, Paul G. and Maskell, Simon (2022) 'Novel Decision Forest Building Techniques by Utilising Correlation Coefficient Methods.', International Conference on Engineering Applications of Neural Networks (EANN 2022) Crete, Greece, 17-20 June 2022.


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.

Item Type:Conference item (Paper)
Full text:Publisher-imposed embargo until 10 June 2023.
(AM) Accepted Manuscript
File format - PDF
Publisher Web site:
Publisher statement:This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at:
Date accepted:31 March 2022
Date deposited:22 June 2022
Date of first online publication:10 June 2022
Date first made open access:10 June 2023

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