The last sub-column of “Labels” represents three different labels according to the value of the variable <time>: “flaring start time” when <time> =“start time”, “flaring peak time” when <time> = “peak time” and “flaring end time” when <time> = “end time”.
For regression methods, the "Multitask" algorithms can use more than one label (merge cell across all the labels), the other ones can just use one label.
The clustering methods, in the prediction phase, assign the data to one of the (un-labelled) clusters estimated in the training phase.
Algorithm | "Type" in the prediction database | Code | Labels | ||||||
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| flaring | N flare | flare index | largest flare | duration flare | flaring <time> | |
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K-Means | "classification" | Python | yes | ||||||
Fuzzy C-Means | "classification" | Python | yes | ||||||
Possibilistic C-Means | "classification" | Python | yes | ||||||
Simulated annealing K-Means | "classification" | Python | yes |
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Simulated annealing Fuzzy C-Means | "classification" | Python | yes |
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Hybrid Lasso | "classification" | Python | yes | ||||||
Hybrid Logit | "probability" | Python | yes | ||||||
SVC CV | "classification" | Python | yes |
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| Random Forest | "probability" | Python | yes | ||||||
MLC Perceptron | "probability" | Python | yes |
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Neural Network | "probability" | R | yes |
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Support Vector Machine | "probability" | R | yes |
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Random Forest | "probability" | R | yes |
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Linear Models | "probability" | R | yes |
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Probit | "probability" | R | yes |
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Logit | "probability" | R | yes |
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MLR Perceptron | "regression" | Python | yes | ||||||
SVR CV | "regression" | Python |
| yes | yes | yes | yes | yes | |
Poisson Multitask | "regression" | Python |
| yes | |||||
Adaptive Poisson Multitask | "regression" | Python |
| yes | |||||
Adaptive Lasso | "regression" | Python |
| yes | yes | yes | yes | yes | |
Multitask Lasso | "regression" | Python |
| yes | |||||
Adaptive Multitask Lasso | "regression" | Python |
| yes | |||||