方法对比
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| 孤立森林 (Isolation Forest)× | 模型校准× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2008 | 2017 |
| 提出者≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. | Platt; Guo et al. |
| 类型≠ | Unsupervised ensemble (random partitioning trees) | Post-hoc probability correction technique |
| 开创性文献≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ | Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. International Conference on Machine Learning, 1321–1330. link ↗ |
| 别名≠ | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection | Classifier Calibration, Probability Calibration, Score Calibration, Model Kalibrasyonu |
| 相关≠ | 5 | 3 |
| 摘要≠ | 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. | Model calibration is a post-hoc technique that adjusts the probability outputs of a trained classifier so that predicted confidence scores match empirical outcome frequencies. A classifier is said to be perfectly calibrated if, among all predictions made with confidence p, exactly a fraction p of them are correct. Systematic miscalibration of modern deep neural networks was rigorously documented by Guo et al. (2017), who showed that networks trained with standard cross-entropy loss tend to be overconfident, and proposed temperature scaling as a simple, effective remedy. |
| ScholarGate数据集 ↗ |
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