Novel Evaluation Techniques for Outlier Detection Methods: A Case Study With RCOD
Outlier detection remains a key challenge in data analysis, with applications spanning cybersecurity, finance, medicine, and more. This paper introduces a comprehensive evaluation framework for comparing outlier detection methods, using the Random Clustering-based Outlier Detector (RCOD) as a case s...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11106501/ |
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| Summary: | Outlier detection remains a key challenge in data analysis, with applications spanning cybersecurity, finance, medicine, and more. This paper introduces a comprehensive evaluation framework for comparing outlier detection methods, using the Random Clustering-based Outlier Detector (RCOD) as a case study. RCOD groups data points around randomly selected cluster centers and identifies outliers based on distance-based criteria and statistical thresholds. To enable more reliable assessment, two novel evaluation strategies are proposed: one based on deviations from the best-performing method per dataset, and another based on rank-based comparison. Experiments conducted on 30 benchmark datasets and 13 detection methods demonstrate RCOD’s superior performance and stability across accuracy, precision, and F1-score metrics. The proposed evaluation techniques provide a deeper insight into the effectiveness of outlier detectors than traditional performance metrics alone. Statistical validation confirms the significance of RCOD’s advantage, highlighting its robustness and applicability to diverse data environments. |
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| ISSN: | 2169-3536 |