Early detection of disease outbreaks and non-outbreaks using incidence data: A framework using feature-based time series classification and machine learning
Fig 2
32 classifiers trained from every unique combination of four synthetic training datasets, two feature extraction libraries, and four predictive models.
The initial letter in each classifier’s name corresponds to the training set (W, WhiteN; E, EnvN; D, DemN; M, MixedN). The middle number represents the feature extraction (22, 22 statistical features; 5, 5 early warning signal indicators). The last letter represents the predictive model (G, GBM; L, LRM; K, KNN; S, SVM).