DES-P

class deslib.des.des_p.DESP(pool_classifiers, k=7, DFP=False, with_IH=False, safe_k=None, IH_rate=0.3, mode='selection')

Dynamic ensemble selection-Performance(des-p). This method selects all base classifiers that achieve a classification performance, in the region of competence, that is higher than the random classifier (RC). The performance of the random classifier is defined by RC = 1/M, where M is the number of classes in the problem.

Parameters:

pool_classifiers : list of classifiers

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

k : int (Default = 7)

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.

mode : String (Default = “selection”)

Whether the technique will perform dynamic selection, dynamic weighting or an hybrid approach for classification.

References

Woloszynski, Tomasz, et al. “A measure of competence based on random classification for dynamic ensemble selection.” Information Fusion 13.3 (2012): 207-213.

Woloszynski, Tomasz, and Marek Kurzynski. “A probabilistic model of classifier competence for dynamic ensemble selection.” Pattern Recognition 44.10 (2011): 2656-2668.

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)

estimate the competence of each base classifier in the pool. The competence level is estimated

based on the classification accuracy of the base classifier for the region of competence.

Parameters:

query : array of shape = [n_features]

The test sample

Returns:

competences : array of shape = [n_classifiers]

The competence level estimated 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 : matrix of shape = [n_samples, n_features] with the data.

y : class labels of each sample 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] with the

probabilities estimates for each class in the classifier model.

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(competences)

Selects all base classifiers that obtained a local classification accuracy higher than the Random Classifier. The performance of the random classifier is denoted 1/L, where L is the number of classes in the problem.

Parameters:

competences : array of shape = [n_classifiers] containing the competence level estimated

for each base classifier.

Returns:

indices : List with the indices of the selected base classifiers.