class deslib.util.knne.KNNE(n_neighbors=7, knn_classifier='sklearn', **kwargs)[source]

” Implementation of the K-Nearest Neighbors-Equality technique.

This implementation fits a different KNN method for each class, and search on each class for the nearest examples.

n_neighbors : int, (default = 7)

Number of neighbors to use by default for kneighbors() queries.

algorithm : str = [‘knn’, ‘faiss]’, (default = ‘knn’)

Whether to use scikit-learn or faiss for nearest neighbors estimation.


Sierra, Basilio, Elena Lazkano, Itziar Irigoien, Ekaitz Jauregi, and Iñigo Mendialdua. “K nearest neighbor equality: giving equal chance to all existing classes.” Information Sciences 181, no. 23 (2011): 5158-5168.

Mendialdua, Iñigo, José María Martínez-Otzeta, I. Rodriguez-Rodriguez, T. Ruiz-Vazquez, and Basilio Sierra. “Dynamic selection of the best base classifier in one versus one.” Knowledge-Based Systems 85 (2015): 298-306.

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.

fit(X, y)[source]

Fit the model according to the given training data.

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.

kneighbors(X=None, n_neighbors=None, return_distance=True)[source]

Finds the K-neighbors of a point. Returns indices of and distances to the neighbors of each point.

X : array-like, shape (n_query, n_features), or (n_query, n_indexed) if metric == ‘precomputed’

The query point or points. If not provided, neighbors of each indexed point are returned. In this case, the query point is not considered its own neighbor.

n_neighbors : int

Number of neighbors to get (default is the value passed to the constructor).

return_distance : boolean, optional. Defaults to True.

If False, distances will not be returned

dist : array

Array representing the lengths to points, only present if return_distance=True

ind : array

Indices of the nearest points in the population matrix.


Predict the class label for each sample in X.

X : array of shape (n_samples, n_features)

The input data.

preds : array, shape (n_samples,)

Class labels for samples in X.


Return probability estimates for the test data X.

X : array-like, shape (n_query, n_features), or (n_query, n_indexed) if metric == ‘precomputed’

Test samples.

proba : array of shape (n_samples, n_classes), or a list of n_outputs

of such arrays if n_outputs > 1. The class probabilities of the input samples. Classes are ordered by lexicographic order.