Classification Based on Decision Tree Algorithm for Machine Learning
By Bahzad Charbuty, Adnan Mohsin Abdulazeez. Journal of Applied Science and Technology Trends, 2021
1,629 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Health Information Management
View Paper: https://www.semanticscholar.org/paper/0d6ef817813d04a3b3ec6c3ce008e104fb3e587a
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Citations in the corpus
- Machine Learning Applications based on SVM Classification A Review (2021, methodology): “From these techniques, SVM is one of the best-known techniques to optimize the expected solution [14].”