Novel evaluation techniques for outlier detection methods: a case study with RCOD
Artykuł w czasopiśmie
MNiSW
100
Lista 2024
| Status: | |
| Autorzy: | Kiersztyn Adam, Kiersztyn Krystyna, Horodelski Michał, Pylak Dorota |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2025 |
| Wersja dokumentu: | Elektroniczna |
| Język: | angielski |
| Wolumen/Tom: | 13 |
| Strony: | 139719 - 139731 |
| Impact Factor: | 3,6 |
| Web of Science® Times Cited: | 0 |
| Scopus® Cytowania: | 0 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 1 sierpnia 2025 |
| Abstrakty: | angielski |
| 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. |
