পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| ব্যাখ্যাযোগ্য আইসোলেশন ফরেস্ট× | ওয়ান-ক্লাস এসভিএম (One-Class SVM)× | |
|---|---|---|
| ক্ষেত্র | যন্ত্র শিখন | যন্ত্র শিখন |
| পরিবার | Machine learning | Machine learning |
| উদ্ভবের বছর≠ | 2008 / 2017 | 1999–2001 |
| প্রবর্তক≠ | 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. |
| ধরন≠ | Anomaly detection with post-hoc explainability | Anomaly / novelty detection (unsupervised) |
| মৌলিক উৎস≠ | 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 ↗ |
| অপর নাম | XIF, Isolation Forest with SHAP, interpretable anomaly detection, explainable anomaly isolation | OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM |
| সম্পর্কিত≠ | 5 | 3 |
| সারসংক্ষেপ≠ | 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. |
| ScholarGateডেটাসেট ↗ |
|
|