rootofdata / LSTM-AE_for_Unsupervised_Outlier_Detection
an innovative framework for indoor air quality outlier detection, comprising three modules: LSTM-AE-based reconstruction error detector, latent feature class-assisted SVM detector, and an ensemble model for robust real-time anomaly detection. Ideal for industrial applications, providing stable and versatile outlier decision rules.
☆21Updated last year
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