The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Classification Based on Decision Tree Algorithm for Machine Learning
Open accessAbstract
Decision tree classifiers are regarded to be a standout of the most well-known methods to data classification representation of classifiers. Different researchers from various fields and backgrounds have considered the problem of extending a decision tree from available data, such as machine study, pattern recognition, and statistics. In various fields such as medical disease analysis, text classification, user smartphone classification, images, and many more the employment of Decision tree classifiers has been proposed in many ways. This paper provides a detailed approach to the decision trees. Furthermore, paper specifics, such as algorithms/approaches used, datasets, and outcomes achieved, are evaluated and outlined comprehensively. In addition, all of the approaches analyzed were discussed to illustrate the themes of the authors and identify the most accurate classifiers. As a result, the uses of different types of datasets are discussed and their findings are analyzed.
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Citations
Cites
Supervised Machine Learning: A Review of Classification Techniques
Kotsiantis, S. B. (2007) · Informatica · Cited by 4,185
Machine learning and the physical sciences
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Machine learning applications in genetics and genomics
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Kotsiantis, S. B., Zaharakis, I. D., & Pintelas, P. E. (2006) · Artificial Intelligence Review · doi:10.1007/s10462-007-9052-3 · Cited by 1,582
A Review on Linear Regression Comprehensive in Machine Learning
Maulud, D., & Abdulazeez, A. M. (2020) · Journal of Applied Science and Technology Trends · doi:10.38094/jastt1457 · Cited by 1,262
Self-citationLim, T.-S., Loh, W.-Y., & Shih, Y.-S. (2000) · Machine Learning · doi:10.1023/a:1007608224229 · Cited by 1,133
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Predicting Diabetes Mellitus With Machine Learning Techniques
Zou, Q., Qu, K., Luo, Y., Yin, D., Ju, Y., & Tang, H. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00515 · Cited by 920
Zebari, R., Abdulazeez, A., Zeebaree, D., Zebari, D., & Saeed, J. (2020) · Journal of Applied Science and Technology Trends · doi:10.38094/jastt1224 · Cited by 918
Self-citation
Cited by
Autonomous driving system: A comprehensive survey
Zhao, J., Zhao, W., Deng, B., Wang, Z., Zhang, F., Zheng, W., Cao, W., Nan, J., Lian, Y., & Burke, A. F. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122836 · Cited by 361
Application of machine learning for advanced material prediction and design
Chan, C. H., Sun, M., & Huang, B. (2022) · EcoMat · doi:10.1002/eom2.12194 · Cited by 123
The AI Future of Emergency Medicine
Petrella, R. J. (2024) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2024.01.031 · Cited by 43
Cantor, M. C., Casella, E., Silvestri, S., Renaud, D. L., & Costa, J. H. C. (2022) · Frontiers in Animal Science · doi:10.3389/fanim.2022.852359 · Cited by 38
Prediction of $$\beta $$-Thalassemia carriers using complete blood count features
Rustam, F., Ashraf, I., Jabbar, S., Tutusaus, K., Mazas, C., Barrera, A. E. P., & de la Torre Diez, I. (2022) · Scientific Reports · doi:10.1038/s41598-022-22011-8 · Cited by 28
Metadata
- Authors
- Charbuty, B., & Abdulazeez, A.
- Venue
- Journal of Applied Science and Technology Trends
- Published
- 2021-03-24
- Volume
- 2
- Issue
- 01
- Pages
- 20-28
- ISSN
- 2708-0757 (ISSN-L)
- OpenAlex
- W3148181069
- Cited by
- 1,871
- References
- 47
- FWCI
- 279.19
- Type
- journal article
- Language
- en
- Open access
- diamond
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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.
