Evaluating the Performance of SVM, Isolation Forest, and DBSCAN for Anomaly Detection
With the advancement of computer technologies, various data models and algorithms have been integrated into industrial processes, significantly improving the efficiency of anomaly detection in datasets while reducing time and energy consumption. Identifying the most effective algorithm for anomaly d...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04012.pdf |
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| _version_ | 1825206602918002688 |
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| author | Lu Haowen |
| author_facet | Lu Haowen |
| author_sort | Lu Haowen |
| collection | DOAJ |
| description | With the advancement of computer technologies, various data models and algorithms have been integrated into industrial processes, significantly improving the efficiency of anomaly detection in datasets while reducing time and energy consumption. Identifying the most effective algorithm for anomaly detection is essential for enhancing nidustrial productivity. This study evaluates the suitability of three algorithms—Support Vector Machine (SVM), Isolation Forest, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) by comparing their accuracy and time efficiency in detecting outliers in different types of datasets. The algorithms are tested across various datasets, and their performance is systematically compared. The results are then analysed in relation to the structure of each algorithm to identify their respective advantages and disadvantages. The study finds that each algorithm performs differently depending on the dataset type. Specifically, SVM demonstrates superior performance in detecting point anomalies, while DBSCAN is more effective when the dataset is pre-processed. Additionally. Isolation Forests are most efficient at identifying collective anomalies within the dataset. |
| format | Article |
| id | doaj-art-eedaebe2a8854035a1bbc8fb9757a13e |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-eedaebe2a8854035a1bbc8fb9757a13e2025-02-07T08:21:11ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700401210.1051/itmconf/20257004012itmconf_dai2024_04012Evaluating the Performance of SVM, Isolation Forest, and DBSCAN for Anomaly DetectionLu Haowen0WLSA Shanghai AcademyWith the advancement of computer technologies, various data models and algorithms have been integrated into industrial processes, significantly improving the efficiency of anomaly detection in datasets while reducing time and energy consumption. Identifying the most effective algorithm for anomaly detection is essential for enhancing nidustrial productivity. This study evaluates the suitability of three algorithms—Support Vector Machine (SVM), Isolation Forest, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) by comparing their accuracy and time efficiency in detecting outliers in different types of datasets. The algorithms are tested across various datasets, and their performance is systematically compared. The results are then analysed in relation to the structure of each algorithm to identify their respective advantages and disadvantages. The study finds that each algorithm performs differently depending on the dataset type. Specifically, SVM demonstrates superior performance in detecting point anomalies, while DBSCAN is more effective when the dataset is pre-processed. Additionally. Isolation Forests are most efficient at identifying collective anomalies within the dataset.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04012.pdf |
| spellingShingle | Lu Haowen Evaluating the Performance of SVM, Isolation Forest, and DBSCAN for Anomaly Detection ITM Web of Conferences |
| title | Evaluating the Performance of SVM, Isolation Forest, and DBSCAN for Anomaly Detection |
| title_full | Evaluating the Performance of SVM, Isolation Forest, and DBSCAN for Anomaly Detection |
| title_fullStr | Evaluating the Performance of SVM, Isolation Forest, and DBSCAN for Anomaly Detection |
| title_full_unstemmed | Evaluating the Performance of SVM, Isolation Forest, and DBSCAN for Anomaly Detection |
| title_short | Evaluating the Performance of SVM, Isolation Forest, and DBSCAN for Anomaly Detection |
| title_sort | evaluating the performance of svm isolation forest and dbscan for anomaly detection |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04012.pdf |
| work_keys_str_mv | AT luhaowen evaluatingtheperformanceofsvmisolationforestanddbscanforanomalydetection |