k-Nearest Output Profiles (KNOP)

class deslib.des.knop.KNOP(pool_classifiers, k=7, DFP=False, with_IH=False, safe_k=None, IH_rate=0.3)

k-Nearest Output Profiles (KNOP).

This method selects all classifiers that correctly classified at least one sample belonging to the region of competence of the query sample. In this case, the region of competence is estimated using the decisions of the base classifier (output profiles). Thus, the similarity between the query and the validation samples are measured in the decision space rather than the feature space. Each selected classifier has a number of votes equals to the number of samples in the region of competence that it predicts the correct label. The votes obtained by all base classifiers are aggregated to obtain the final ensemble decision.

Parameters:

pool_classifiers : type, the generated_pool of classifiers trained for the corresponding

classification problem.

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.

References

Cavalin, Paulo R., Robert Sabourin, and Ching Y. Suen. “LoGID: An adaptive framework combining local and global incremental learning for dynamic selection of ensembles of HMMs.” Pattern Recognition 45.9 (2012): 3544-3556.

Cavalin, Paulo R., Robert Sabourin, and Ching Y. Suen. “Dynamic selection approaches for multiple classifier systems.” Neural Computing and Applications 22.3-4 (2013): 673-688.

Ko, Albert HR, Robert Sabourin, and Alceu Souza Britto Jr. “From dynamic classifier selection to dynamic ensemble selection.” Pattern Recognition 41.5 (2008): 1718-1731.

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_from_proba(query, probabilities)

The competence of the base classifiers is simply estimated as the number of samples in the region of competence that it correctly classified. This method received an array with the pre-calculated probability estimates for each query.

This information is later used to determine the number of votes obtained for each base classifier.

Parameters:

query : array of shape = [n_samples, n_features]

The test examples.

probabilities : array of shape = [n_samples, n_classifiers, n_classes]

Probabilities estimates obtained by each each base classifier for each query sample.

Returns:

competences : array of shape = [n_samples, n_classifiers]

Competence level estimated for each base classifier and test example.

fit(X, y)

Train the DS model by setting the KNN algorithm and pre-process the information required to apply the DS methods. In this case, the scores of the base classifiers for the dynamic selection dataset (DSEL) are pre-calculated to transform each sample in DSEL into an output profile.

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.

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(competences)

Select the base classifiers for the classification of the query sample.

Each base classifier can be selected more than once. The number of times a base classifier is selected (votes) is equals to the number of samples it correctly classified in the region of competence.

Parameters:

competences : array of shape = [n_samples, n_classifiers]

Competence level estimated for each base classifier and test example.

Returns:

selected_classifiers : array of shape = [n_samples, n_classifiers]

Boolean matrix containing True if the base classifier is select, False otherwise.