DES-Minimum Difference

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

Computes the competence level of the classifiers based on the difference between the support obtained by each class. The competence level at a data point (xk) is equal to the minimum difference between the support obtained to the correct class and the support obtained for different classes.

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()

Calculates the source of competence using the Minimum Difference method.

The source of competence C_src at the validation point xk calculated by the Minimum Difference between the supports obtained to the correct class and the support obtained by the other classes

Returns:

C_src : array of shape = [n_samples, n_classifiers]

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