diff --git a/quapy/method/aggregative.py b/quapy/method/aggregative.py
index 4cbe728..ad96e53 100644
--- a/quapy/method/aggregative.py
+++ b/quapy/method/aggregative.py
@@ -7,6 +7,8 @@ from sklearn.base import BaseEstimator
 from sklearn.calibration import CalibratedClassifierCV
 from sklearn.metrics import confusion_matrix
 from sklearn.model_selection import cross_val_predict
+from typing_extensions import override
+
 import quapy as qp
 import quapy.functional as F
 from functional import get_divergence
@@ -19,7 +21,7 @@ from quapy.method.base import BaseQuantifier, BinaryQuantifier, OneVsAllGeneric
 # Abstract classes
 # ------------------------------------
 
-class AggregativeQuantifier(ABC, BaseQuantifier):
+class AggregativeQuantifier(BaseQuantifier, ABC):
     """
     Abstract class for quantification methods that base their estimations on the aggregation of classification
     results. Aggregative quantifiers implement a pipeline that consists of generating classification predictions
@@ -65,7 +67,8 @@ class AggregativeQuantifier(ABC, BaseQuantifier):
         """
         assert isinstance(fit_classifier, bool), 'unexpected type for "fit_classifier", must be boolean'
 
-        self.__check_classifier(adapt_if_necessary=(self.__classifier_method=='predict_proba'))
+        print(type(self))
+        self.__check_classifier(adapt_if_necessary=(self.__classifier_method()=='predict_proba'))
 
         if predict_on is None:
             if fit_classifier:
@@ -149,12 +152,12 @@ class AggregativeQuantifier(ABC, BaseQuantifier):
         """
         return self.classifier.predict(instances)
 
-    @property
     def __classifier_method(self):
+        print('using predict')
         return 'predict'
 
     def __check_classifier(self, adapt_if_necessary=False):
-        assert hasattr(self.classifier, 'predict')
+        assert hasattr(self.classifier, self.__classifier_method())
 
     def quantify(self, instances):
         """
@@ -199,12 +202,12 @@ class AggregativeProbabilisticQuantifier(AggregativeQuantifier, ABC):
     def classify(self, instances):
         return self.classifier.predict_proba(instances)
 
-    @property
     def __classifier_method(self):
+        print('using predict_proba')
         return 'predict_proba'
 
     def __check_classifier(self, adapt_if_necessary=False):
-        if not hasattr(self.classifier, 'predict_proba'):
+        if not hasattr(self.classifier, self.__check_classifier()):
             if adapt_if_necessary:
                 print(f'warning: The learner {self.classifier.__class__.__name__} does not seem to be '
                       f'probabilistic. The learner will be calibrated (using CalibratedClassifierCV).')