Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Aktiivisen oppimisen eristysmetsä× | Isolation Forest× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2008–2019 | 2008 |
| Kehittäjä≠ | Das, S. et al. (active anomaly discovery framework); Liu, F. T. et al. (Isolation Forest base) | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Tyyppi≠ | Active learning wrapper over isolation forest anomaly detector | Unsupervised ensemble (random partitioning trees) |
| Alkuperäislähde≠ | Das, S., Wong, W. K., Fern, A., Dietterich, T. G., & Amran Siddiqui, M. (2019). Incorporating Expert Feedback into Active Anomaly Discovery. In Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM), pp. 1009–1014. link ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Rinnakkaisnimet≠ | AL-iForest, active anomaly detection with isolation forest, active isolation forest, query-guided isolation forest | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | Active Learning Isolation Forest combines the unsupervised anomaly-scoring power of Isolation Forest with an iterative query strategy that asks a human expert to label the most informative instances. The result is a detector that refines its anomaly boundaries using a minimal labeling budget, dramatically improving precision on rare and subtle anomalies compared to a purely unsupervised baseline. | Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets. |
| ScholarGateAineisto ↗ |
|
|