DES-KNN

class deslib.des.des_knn.DESKNN(pool_classifiers, k=7, DFP=False, with_IH=False, safe_k=None, IH_rate=0.3, pct_accuracy=0.5, pct_diversity=0.3, more_diverse=True, metric='DF')

Dynamic ensemble Selection KNN (DES-KNN).

This method selects an ensemble of classifiers taking into account the accuracy and diversity of the base classifiers. The k-NN algorithm is used to define the region of competence. The N most accurate classifiers in the region of competence are first selected. Then, the J more diverse classifiers from the N most accurate classifiers are selected to compose the ensemble.

Parameters:

pool_classifiers : list of classifiers

The generated_pool of classifiers trained for the corresponding classification problem. The classifiers should support the method “predict”.

k : int (Default = 5)

Number of neighbors used to estimate the competence of the base classifiers.

DFP : Boolean (Default = False)

Determines if the dynamic frienemy pruning is applied.

with_IH : Boolean (Default = False)

Whether the hardness level of the region of competence is used to decide between using the DS algorithm or the KNN for classification of a given query sample.

safe_k : int (default = None)

The size of the indecision region.

IH_rate : float (default = 0.3)

Hardness threshold. If the hardness level of the competence region is lower than the IH_rate the KNN classifier is used. Otherwise, the DS algorithm is used for classification.

pct_accuracy : float (Default = 0.5)

Percentage of base classifiers selected based on accuracy

pct_diversity : float (Default = 0.3)

Percentage of base classifiers selected based n diversity

more_diverse : Boolean (Default = True)

Whether we select the most or the least diverse classifiers to add to the pre-selected ensemble

metric : String (Default = ‘df’)

Diversity diversity_func used to estimate the diversity of the base classifiers. Can be either the double fault (df), Q-statistics (Q), or error correlation (corr)

References

Soares, R. G., Santana, A., Canuto, A. M., & de Souto, M. C. P. “Using accuracy and more_diverse to select classifiers to build ensembles.” International Joint Conference on Neural Networks (IJCNN)., 2006.

Britto, Alceu S., Robert Sabourin, and Luiz ES Oliveira. “Dynamic selection of classifiers—a comprehensive review.” Pattern Recognition 47.11 (2014): 3665-3680.

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.

estimate_competence(query, predictions=None)

estimate the competence level of each base classifier \(c_{i}\) for the classification of the query sample.

The competence is estimated using the accuracy and diversity criteria. First the classification accuracy of the base classifiers in the region of competence is estimated. Then the diversity of the base classifiers in the region of competence is estimated.

The method returns two arrays: One containing the accuracy and the other the diversity of each base classifier.

Parameters:

query : array cf shape = [n_samples, n_features]

The query sample.

predictions : array of shape = [n_samples, n_classifiers]

Predictions of the base classifiers for all test examples.

Returns:

accuracy : array of shape = [n_samples, n_classifiers}

Local Accuracy estimates (competences) of the base classifiers for all query samples.

diversity : array of shape = [n_samples, n_classifiers}

Average pairwise diversity of each base classifiers for all test examples.

Notes

This technique uses both the accuracy and diversity information to perform dynamic selection. For this reason the function returns a dictionary containing these two values instead of a single ndarray containing the competence level estimates for each base classifier.

fit(X, y)

Prepare the DS model by setting the KNN algorithm and pre-processing the information required to apply the DS methods

Parameters:

X : array of shape = [n_samples, n_features]

The input data.

y : array of shape = [n_samples]

class labels of each example in X.

Returns:

self

predict(X)

Predict the class label for each sample in X.

Parameters:

X : array of shape = [n_samples, n_features]

The input data.

Returns:

predicted_labels : array of shape = [n_samples]

Predicted class label for each sample in X.

predict_proba(X)

Estimates the posterior probabilities for sample in X.

Parameters:

X : array of shape = [n_samples, n_features]

The input data.

Returns:

predicted_proba : array of shape = [n_samples, n_classes]

Probabilities estimates for each sample in X.

score(X, y, sample_weight=None)

Returns the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

X : array-like, shape = (n_samples, n_features)

Test samples.

y : array-like, shape = (n_samples) or (n_samples, n_outputs)

True labels for X.

sample_weight : array-like, shape = [n_samples], optional

Sample weights.

Returns:

score : float

Mean accuracy of self.predict(X) wrt. y.

select(accuracy, diversity)

Select an ensemble containing the N most accurate ant the J most diverse classifiers for the classification of the query sample.

Parameters:

accuracy : array of shape = [n_samples, n_classifiers]

Local Accuracy estimates (competence) of each base classifiers for all query samples.

diversity : array of shape = [n_samples, n_classifiers]

Average pairwise diversity of each base classifiers for all test examples.

Returns:

selected_classifiers : array of shape = [n_samples, self.J]

Matrix containing the indices of the J selected base classifier for each test example.