Classification Based on Decision Tree Algorithm for Machine Learning

TLDR

Decision tree classifiers are widely used for data classification across domains such as medicine, text, and image analysis. The study presents a comprehensive approach to decision‑tree classification. The authors evaluate various decision‑tree algorithms, datasets, and performance results, comparing approaches to identify the most accurate classifiers. The paper discusses how different datasets influence decision‑tree performance and analyzes the resulting findings.

Abstract

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.