Frienemy Pruning

Implementation of the Dynamic Frienemy Pruning (DFP) algorithm for online pruning of base classifiers.

References

Oliveira, D.V.R., Cavalcanti, G.D.C. and Sabourin, R., Online Pruning of Base Classifiers for Dynamic Ensemble Selection, Pattern Recognition, vol. 72, December 2017, pp 44-58.

Cruz, Rafael MO, Dayvid VR Oliveira, George DC Cavalcanti, and Robert Sabourin. “FIRE-DES++: Enhanced online pruning of base classifiers for dynamic ensemble selection.” Pattern Recognition 85 (2019): 149-160.

deslib.util.dfp.frienemy_pruning(X_query, X_dsel, y_dsel, ensemble, k)[source]

Implements the Online Pruning method (frienemy) which prunes base classifiers that do not cross the region of competence of a given instance. A classifier crosses the region of competence if it correctly classify at least one sample for each different class in the region.

Parameters:
X_query : array-like of shape (n_samples, n_features)

Test set.

X_dsel : array-like of shape (n_samples, n_features)

Dynamic selection set.

y_dsel : array-like of shape (n_samples,)

The target values (Dynamic selection set).

ensemble : list of shape = [n_classifiers]

The ensemble of classifiers to be pruned.

k : int

Number of neighbors used to compute the regions of competence.
Returns:
DFP_mask : array-like of shape = [n_samples, n_classifiers]

Mask containing 1 for the selected base classifier and 0 otherwise.
deslib.util.dfp.frienemy_pruning_preprocessed(neighbors, y_val, hit_miss)[source]

Implements the Online Pruning method (frienemy) which prunes base classifiers that do not cross the region of competence of a given instance. A classifier crosses the region of competence if it correctly classify at least one sample for each different class in the region.

Parameters:
neighbors : array-like of shape (n_samples, n_neighbors)

Indices of the k nearest neighbors.

y_val : array-like of shape (n_samples,)

The target values (class labels).

hit_miss : array-like of shape (n_samples, n_classifiers)

Matrix containing 1 when the base classifier made the correct prediction, 0 otherwise.
Returns:
DFP_mask : array-like of shape = [n_samples, n_classifiers]

Mask containing 1 for the selected base classifier and 0 otherwise.

Notes

This implementation assumes the regions of competence of each query example (neighbors) and the predictions for the dynamic selection data (hit_miss) were already pre-computed.