DES-Exponential

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

The source of competence C_src at the validation point xk is a product of two factors: The absolute value of the competence and the sign. The value of the source competence is inverse proportional to the normalized entropy of its supports vector. The sign of competence is simply determined by correct/incorrect classification of xk [1].

The influence of each sample xk is defined according to a Gaussian function model[2]. Samples that are closer to the query have a higher influence in the competence estimation.

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 = 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

B. Antosik, M. Kurzynski, New measures of classifier competence – heuristics and application to the design of multiple classifier systems., in: Computer recognition systems 4., 2011, pp. 197–206.

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

source_competence()[source]

The source of competence C_src at the validation point xk is a product of two factors: The absolute value of the competence and the sign. The value of the source competence is inverse proportional to the normalized entropy of its supports vector.The sign of competence is simply determined by correct/incorrect classification of the instance xk.

Returns:
C_src : array of shape = [n_samples, n_classifiers]

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