The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
A Survey of Decision Trees: Concepts, Algorithms, and Applications
Open accessAbstract
Machine learning (ML) has been instrumental in solving complex problems and significantly advancing different areas of our lives. Decision tree-based methods have gained significant popularity among the diverse range of ML algorithms due to their simplicity and interpretability. This paper presents a comprehensive overview of decision trees, including the core concepts, algorithms, applications, their early development to the recent high-performing ensemble algorithms and their mathematical and algorithmic representations, which are lacking in the literature and will be beneficial to ML researchers and industry experts. Some of the algorithms include classification and regression tree (CART), Iterative Dichotomiser 3 (ID3), C4.5, C5.0, Chi-squared Automatic Interaction Detection (CHAID), conditional inference trees, and other tree-based ensemble algorithms, such as random forest, gradient-boosted decision trees, and rotation forest. Their utilisation in recent literature is also discussed, focusing on applications in medical diagnosis and fraud detection.
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Cites
1 retracted
Shamrat, F. M. J. M., Chakraborty, S., Billah, M. M., Das, P., Muna, J. N., & Ranjan, R. (2021) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei51242.2021.9452898 · Cited by 35
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Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
Unbiased Recursive Partitioning: A Conditional Inference Framework
Hothorn, T., Hornik, K., & Zeileis, A. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x133933 · Cited by 4,021
Conditional variable importance for random forests
Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., & Zeileis, A. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-307 · Cited by 3,254
An exploratory technique for investigating large quantities of categorical data
Kass, G. V. (1980) · Applied Statistics · doi:10.2307/2986296 · Cited by 2,880
A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects
Mienye, I. D., & Sun, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3207287 · Cited by 1,298
Self-citationBertsimas, D., & Dunn, J. (2017) · Machine Learning · doi:10.1007/s10994-017-5633-9 · Cited by 638
Cited by
Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications
Mienye, I. D., Swart, T. G., & Obaido, G. (2024) · Information · doi:10.3390/info15090517 · Cited by 732
Self-citationMienye, I. D., Obaido, G., Jere, N., Mienye, E., Aruleba, K., Emmanuel, I. D., & Ogbuokiri, B. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101587 · Cited by 212
Self-citationThe Use of Machine Learning Models with Optuna in Disease Prediction
Lai, L.-H., Lin, Y.-L., Liu, Y.-H., Lai, J.-P., Yang, W.-C., Hou, H.-P., & Pai, P.-F. (2024) · Electronics · doi:10.3390/electronics13234775 · Cited by 30
Metadata
- Authors
- Mienye, I. D., & Jere, N.
- Venue
- IEEE Access
- Published
- 2024
- Volume
- 12
- Pages
- 86716-86727
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4399800820
- Cited by
- 353
- References
- 69
- FWCI
- 135.09
- Type
- journal article
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
