Efthyvoulos Drousiotis
Novel Decision Forest Building Techniques by Utilising Correlation Coefficient Methods
Drousiotis, Efthyvoulos; Shi, Lei; Spirakis, Paul G.; Maskell, Simon
Authors
Lei Shi
Paul G. Spirakis
Simon Maskell
Contributors
Lazaros Iliadis
Editor
Chrisina Jayne
Editor
Anastasios Tefas
Editor
Elias Pimenidis
Editor
Abstract
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.
Citation
Drousiotis, E., Shi, L., Spirakis, P. G., & Maskell, S. (2022). Novel Decision Forest Building Techniques by Utilising Correlation Coefficient Methods. In L. Iliadis, C. Jayne, A. Tefas, & E. Pimenidis (Eds.), Engineering Applications of Neural Networks: 23rd International Conference, EAAAI/EANN 2022, Chersonissos, Crete, Greece, June 17–20, 2022, Proceedings (90-102). Springer, Cham. https://doi.org/10.1007/978-3-031-08223-8_8
Acceptance Date | Mar 31, 2022 |
---|---|
Online Publication Date | Jun 10, 2022 |
Publication Date | 2022 |
Deposit Date | Jun 21, 2022 |
Publicly Available Date | Mar 29, 2024 |
Pages | 90-102 |
Series Title | Communications in Computer and Information Science |
Series Number | 1600 |
Book Title | Engineering Applications of Neural Networks: 23rd International Conference, EAAAI/EANN 2022, Chersonissos, Crete, Greece, June 17–20, 2022, Proceedings |
ISBN | 978-3-031-08222-1 |
DOI | https://doi.org/10.1007/978-3-031-08223-8_8 |
Related Public URLs | https://livrepository.liverpool.ac.uk/3154575/ |
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Copyright 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: https://doi.org/10.1007/978-3-031-08223-8_8
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