Formulation of K-nearest neighbor model by varying the distance metrics of mahalanobis, correlation and cosine in discriminating different grades of Aquilaria oil
This study formulates a K-Nearest Neighbor (K-NN) model for the classification of Aquilaria (agarwood) oil grades by varying distance metrics: Mahalanobis, Correlation and Cosine. A total of 96 agarwood oil samples were analyzed using Gas chromatography-mass spectrometry (GC-MS), identifying key che...
| 发表在: | JOURNAL OF ESSENTIAL OIL BEARING PLANTS |
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| Main Authors: | , , , |
| 格式: | Article; Early Access |
| 语言: | English |
| 出版: |
TAYLOR & FRANCIS LTD
2025
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| 主题: | |
| 在线阅读: | https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001437517500001 |
| 总结: | This study formulates a K-Nearest Neighbor (K-NN) model for the classification of Aquilaria (agarwood) oil grades by varying distance metrics: Mahalanobis, Correlation and Cosine. A total of 96 agarwood oil samples were analyzed using Gas chromatography-mass spectrometry (GC-MS), identifying key chemical compounds contributing to quality differentiation. The proposed model achieved 100% classification accuracy at k = 1 across all distance metrics with Mahalanobis distance consistently outperforming others as k increased. Cross-validation results demonstrated the lowest error rates for Mahalanobis followed by Correlation and Cosine, confirming its robustness in high-dimensional chemical datasets. These findings highlight the effectiveness of machine learning in agarwood oil grading and underscore the importance of selecting optimal distance metrics for improved classification accuracy. Future work could incorporate ensemble learning and advanced feature selection to further refine performance. |
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| ISSN: | 0972-060X 0976-5026 |
| DOI: | 10.1080/0972060X.2025.2469677 |