Version 1
: Received: 16 August 2024 / Approved: 19 August 2024 / Online: 20 August 2024 (10:42:40 CEST)
How to cite:
Salman, O. A.; Hosszú, G. Evaluating Feature Impact Prior to Phylogenetic Analysis Using Machine Learning Techniques. Preprints2024, 2024081320. https://doi.org/10.20944/preprints202408.1320.v1
Salman, O. A.; Hosszú, G. Evaluating Feature Impact Prior to Phylogenetic Analysis Using Machine Learning Techniques. Preprints 2024, 2024081320. https://doi.org/10.20944/preprints202408.1320.v1
Salman, O. A.; Hosszú, G. Evaluating Feature Impact Prior to Phylogenetic Analysis Using Machine Learning Techniques. Preprints2024, 2024081320. https://doi.org/10.20944/preprints202408.1320.v1
APA Style
Salman, O. A., & Hosszú, G. (2024). Evaluating Feature Impact Prior to Phylogenetic Analysis Using Machine Learning Techniques. Preprints. https://doi.org/10.20944/preprints202408.1320.v1
Chicago/Turabian Style
Salman, O. A. and Gábor Hosszú. 2024 "Evaluating Feature Impact Prior to Phylogenetic Analysis Using Machine Learning Techniques" Preprints. https://doi.org/10.20944/preprints202408.1320.v1
Abstract
The purpose of this paper is to describe a feature selection algorithm and its application to enhance the accuracy of the reconstruction of phylogenetic trees by improving the efficiency of tree construction. Applying machine learning models for Arabic and Aramaic scripts such as Deep Neural Networks (DNN), Support Vector Machines (SVM) and Random Forests (RF) each model was used to compare the phylogenies. The methodology was utilized with a dataset containing Arabic and Aramaic scripts, demonstrating its relevance in a range of phylogenetic analyses. Results emphasize the essential role of feature selection by DNNs is outperforming other models in terms of Area Under the Curve (AUC) and Equal Error Rate (EER) across various datasets and fold sizes. Additionally, SVM and RF models show important understandings, advantages and limits of each approach within the context of phylogenetic analysis. This method not only simplifies the tree structures but also enhances their Consistency Index. Therefore offering a robust framework for evolutionary studies. The findings highlight the applications of machine learning in phylogenetics suggesting a path toward accurate and efficient evolutionary analyses and adding a deeper understanding of evolutionary relationships.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
