DES-Logarithmic¶
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class
deslib.des.probabilistic.Logarithmic(pool_classifiers, k=None, DFP=False, with_IH=False, safe_k=None, IH_rate=0.3, mode='selection')[source]¶ This method estimates the competence of the classifier based on the logarithmic difference between the supports obtained by the base classifier.
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.
T.Woloszynski, M. Kurzynski, A measure of competence based on randomized reference classifier for dynamic ensemble selection, in: International Conference on Pattern Recognition (ICPR), 2010, pp. 4194–4197.