Randomized Reference Classifier (RRC)

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

DES technique based on the Randomized Reference Classifier method (DES-RRC).

Parameters:

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

classification problem.

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 = None)

Number of neighbors used to estimate the competence of the base classifiers. If k = None, the whole dynamic selection dataset is used, and the influence of each sample is based on its distance to the query.

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

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.

source_competence()

Calculates the source of competence using the randomized reference classifier (RRC) method.

The source of competence C_src at the validation point xk calculated using the probabilistic model based on the supports obtained by the base classifier and randomized reference classifier (RRC) model. The probabilistic modeling of the classifier competence is calculated using the ccprmod function.

Returns:

C_src : array of shape = [n_samples, n_classifiers]

The competence source for each base classifier at each data point.