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Novel Decision Forest Building Techniques by Utilising Correlation Coefficient Methods

Drousiotis, Efthyvoulos; Shi, Lei; Spirakis, Paul G.; Maskell, Simon

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Authors

Efthyvoulos Drousiotis

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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