Static Selection¶
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class
deslib.static.static_selection.StaticSelection(pool_classifiers, pct_classifiers=0.5)[source]¶ Ensemble model that selects N classifiers with the best performance in a dataset
Parameters: - pool_classifiers : list of classifiers
The generated_pool of classifiers trained for the corresponding classification problem. The classifiers should support methods “predict”.
- pct_classifiers : float (Default = 0.5)
percentage of base classifier that should be selected by the selection scheme.
References
Britto, Alceu S., Robert Sabourin, and Luiz ES Oliveira. “Dynamic selection of classifiers—a comprehensive review.” Pattern Recognition 47.11 (2014): 3665-3680.
Kuncheva, Ludmila I. Combining pattern classifiers: methods and algorithms. John Wiley & Sons, 2004.
R. M. O. Cruz, R. Sabourin, and G. D. Cavalcanti, “Dynamic classifier selection: Recent advances and perspectives,” Information Fusion, vol. 41, pp. 195 – 216, 2018.
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fit(X, y)[source]¶ Fit the static selection model by select an ensemble of classifier containing the base classifiers with highest accuracy in the given dataset.
Parameters: - X : array of shape = [n_samples, n_features]
Data used to fit the model.
- y : array of shape = [n_samples]
class labels of each example in X.