异常检测
Statistics and Machine Learning Toolbox™ 为未标注的多元样本数据提供了几种异常检测方法。您还可以使用一类支持向量机 (SVM) 和稳健的随机切割森林算法(请参阅Incremental Anomaly Detection Overview)来检测流数据中的异常。异常检测通过训练模型或学习参数来检测离群值(训练数据中的异常)。对于新奇值检测(使用未污染的训练数据检测新数据中的异常),您可以使用未污染的训练数据(没有离群值的数据)训练模型或学习参数,并通过使用训练的模型或学习的参数来检测新数据中的异常。有关详细信息,请参阅Unsupervised Anomaly Detection。
如果将训练数据标注为正常点和异常点,您可以训练二类分类模型,并使用 resubPredict
和 predict
对象函数分别检测训练数据和新数据中的异常。有关支持的分类方法列表,请参阅分类。
该工具箱还提供了特定于模型的异常检测方法,您可以在训练分类、回归或聚类模型后应用这些方法。有关详细信息,请参阅 Model-Specific Anomaly Detection。
函数
对象
IsolationForest | Isolation forest for anomaly detection (自 R2021b 起) |
RobustRandomCutForest | Robust random cut forest model for anomaly detection (自 R2023a 起) |
LocalOutlierFactor | Local outlier factor model for anomaly detection (自 R2022b 起) |
OneClassSVM | One-class support vector machine (SVM) for anomaly detection (自 R2022b 起) |
incrementalRobustRandomCutForest | Robust random cut forest model for incremental anomaly detection (自 R2023b 起) |
incrementalOneClassSVM | One-class support vector machine (SVM) model for incremental anomaly detection (自 R2023b 起) |
主题
- Unsupervised Anomaly Detection
Detect anomalies using isolation forest, robust random cut forest, local outlier factor, one-class SVM, and Mahalanobis distance.
- Incremental Anomaly Detection Overview
Discover fundamental concepts about incremental anomaly detection in streaming data.
- Configure Model for Incremental Anomaly Detection
Prepare a model for incremental anomaly detection and fitting on a data stream.
- Anomaly Detection with Isolation Forest
Detect anomalies by isolating anomalies from normal points using an isolation forest (ensemble of isolation trees).
- Model-Specific Anomaly Detection
After training a classification, regression, or clustering model, detect anomalies using a model-specific anomaly detection feature.
- Build Condition Model for Industrial Machinery and Manufacturing Processes
Train a binary classification model using Classification Learner App to detect anomalies in sensor data collected from an industrial manufacturing machine.
- Code Generation for Anomaly Detection
Generate single-precision code that detects anomalies in data using a trained isolation forest model or one-class SVM.
相关信息
- Anomaly Detection in Industrial Machinery Using Three-Axis Vibration Data (Predictive Maintenance Toolbox)