An Alternative Method for Upgrading the Conventional Decision Tree Algorithm

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dc.contributor.authorKirdponpattara, Suppakrit
dc.contributor.authorBoonjing, Veera
dc.contributor.authorSooraksa, Pitikhate
dc.date.accessioned2026-08-06T10:44:43Z
dc.date.available2026-08-06T10:44:43Z
dc.date.issued2024-01-01
dc.description.abstractDecision tree algorithms are widely used for solving classification and regression problems. Their popularity can be attributed to their transparent nature, simplicity, easy interpretability, faster classification speed, and strong decision rules. However, decision tree induction algorithms face various inherent and external limitations, such as overfitting, high sensitivity to noise and outliers, and instability with minimal data variations. In this study, we introduce an innovative approach to enhance traditional decision tree algorithms [e.g., Iterative Dichotomiser 3 (ID3), C4.5, and Classification and Regression Trees (CART)] by incorporating feature selection techniques. The proposed approach aims to enhance the accuracy and efficiency of decision tree models. Experiments were conducted on a real-world dataset of a hard disk drive (HDD) manufacturing process using the proposed approach. In comparison with a baseline where all features were utilized, the study highlighted a significant improvement in accuracy, indicating that the approach holds immense potential for optimizing decision tree algorithms and improving the HDD manufacturing process.
dc.identifier.citationSensors and Materials, 36(4), 1461-1471, 2024
dc.identifier.doi10.18494/SAM5029
dc.identifier.issn09144935
dc.identifier.other2-s2.0-85191572412
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/15277
dc.sourceSensors and Materials
dc.subjectdecision tree
dc.subjectdefect prediction
dc.subjectfeature selection
dc.subjecthard disk drive manufacturing
dc.titleAn Alternative Method for Upgrading the Conventional Decision Tree Algorithm
dc.typeArticle

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