পদ্ধতির তুলনা করুন

	রোবাস্ট আইসোলেশন ফরেস্ট	দৃঢ় ওয়ান-ক্লাস এসভিএম
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2008–2019	2000s–2010s
প্রবর্তক	Liu, F. T., Ting, K. M., Zhou, Z.-H. (base); robust extensions by multiple authors	Extensions of Scholkopf et al. (1999); robust variants developed in 2000s–2010s
ধরন	Robust ensemble anomaly detection	Anomaly detection / novelty detection
মৌলিক উৎস	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the IEEE International Conference on Data Mining (ICDM), pp. 413–422. IEEE.	Scholkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., & Platt, J. (1999). Support vector method for novelty detection. Advances in Neural Information Processing Systems (NeurIPS), 12, 582–588.
অপর নাম	Robust iForest, noise-robust isolation forest, contamination-robust isolation forest, robust anomaly isolation	Robust OCSVM, Outlier-robust One-Class SVM, Contamination-tolerant OCSVM, Robust novelty detection SVM
সম্পর্কিত	5	5
সারসংক্ষেপ	Robust Isolation Forest extends the classic Isolation Forest anomaly detector with strategies that reduce sensitivity to data contamination, masking effects, and biased random splits. By incorporating robustness mechanisms — such as improved subsampling, re-weighting of suspicious regions, or bias-corrected splitting — it achieves more reliable anomaly scores when the training data itself contains a non-trivial fraction of anomalies or when specific feature distributions cause standard iForest to produce unreliable path lengths.	Robust One-Class SVM extends the classic One-Class Support Vector Machine for novelty and anomaly detection by incorporating robustness mechanisms — such as trimmed objectives, robust kernel choices, or contamination-tolerant loss functions — that reduce the influence of heavy-tailed noise or outliers present in the training data, yielding a decision boundary that better represents the true support of the normal class.