Bandingkan metode
Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.
| Isolation Forest yang Dapat Dijelaskan× | SVM Satu Kelas× | |
|---|---|---|
| Bidang | Pembelajaran Mesin | Pembelajaran Mesin |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 2008 / 2017 | 1999–2001 |
| Pencetus≠ | Liu, F. T., Ting, K. M., & Zhou, Z.-H. (Isolation Forest); Lundberg, S. M. & Lee, S.-I. (SHAP explainability layer) | Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C. |
| Tipe≠ | Anomaly detection with post-hoc explainability | Anomaly / novelty detection (unsupervised) |
| Sumber perintis≠ | Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. link ↗ | Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471. DOI ↗ |
| Alias | XIF, Isolation Forest with SHAP, interpretable anomaly detection, explainable anomaly isolation | OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM |
| Terkait≠ | 5 | 3 |
| Ringkasan≠ | Explainable Isolation Forest combines the Isolation Forest anomaly detection algorithm with post-hoc explainability tools — most commonly SHAP (SHapley Additive exPlanations) — to not only flag anomalous observations but also reveal which features drove each anomaly score. It bridges unsupervised anomaly detection with the interpretability demands of regulated and high-stakes domains. | One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the single-class setting where no labelled anomalies are available. |
| ScholarGateSet data ↗ |
|
|