ec396411be11d514a44a18813278f3a41c73ac5f,deslib/des/meta_des.py,METADES,__init__,#METADES#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,86

Before Change

        self.Hc = Hc
        self.selection_threshold = selection_threshold

        if meta_classifier is None:
            warnings.warn("No classifier model passed for the Meta-Classifier. Using a Naive Bayes instead")
            self.meta_classifier = MultinomialNB()
        else:
            self.meta_classifier = meta_classifier

        self.op_knn = None
        self.n_meta_features = (self.k * 2) + self.Kp + 2

    def fit(self, X, y):

After Change

    Cruz, R.M., Sabourin, R., Cavalcanti, G.D. and Ren, T.I., 2015. META-DES: A dynamic ensemble selection framework
    using meta-learning. Pattern Recognition, 48(5), pp.1925-1935.

    Cruz, R.M., Sabourin, R. and Cavalcanti, G.D., 2015, July. META-des. H: a dynamic ensemble selection technique
    using meta-learning and a dynamic weighting approach. In Neural Networks (IJCNN), 2015 International Joint
    Conference on (pp. 1-8).

    R. M. O. Cruz, R. Sabourin, and G. D. Cavalcanti, “Dynamic classifier selection: Recent advances and perspectives,”

Instances