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 \(\mathbf{x}_{k}\) 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

[1] 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.

[2] 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 \(\mathbf{x}_{k}\) 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.