diff --git a/imblearn/ensemble/_easy_ensemble.py b/imblearn/ensemble/_easy_ensemble.py
index 29f0095f2..2211f5cc1 100644
--- a/imblearn/ensemble/_easy_ensemble.py
+++ b/imblearn/ensemble/_easy_ensemble.py
@@ -5,6 +5,7 @@
 # License: MIT
 
 import copy
+import inspect
 import numbers
 
 import numpy as np
@@ -13,7 +14,6 @@
 from sklearn.ensemble._bagging import _parallel_decision_function
 from sklearn.ensemble._base import _partition_estimators
 from sklearn.utils._param_validation import Interval, StrOptions
-from sklearn.utils._tags import _safe_tags
 from sklearn.utils.fixes import parse_version
 from sklearn.utils.metaestimators import available_if
 from sklearn.utils.parallel import Parallel, delayed
@@ -312,11 +312,16 @@ def decision_function(self, X):
         # Parallel loop
         n_jobs, _, starts = _partition_estimators(self.n_estimators, self.n_jobs)
 
+        kwargs = {}
+        if "params" in inspect.signature(_parallel_decision_function).parameters:
+            kwargs["params"] = {}
+
         all_decisions = Parallel(n_jobs=n_jobs, verbose=self.verbose)(
             delayed(_parallel_decision_function)(
                 self.estimators_[starts[i] : starts[i + 1]],
                 self.estimators_features_[starts[i] : starts[i + 1]],
                 X,
+                **kwargs,
             )
             for i in range(n_jobs)
         )
@@ -343,7 +348,7 @@ def _get_estimator(self):
         return self.estimator
 
     def _more_tags(self):
-        return {"allow_nan": _safe_tags(self._get_estimator(), "allow_nan")}
+        return {"allow_nan": get_tags(self._get_estimator()).input_tags.allow_nan}
 
     def __sklearn_tags__(self):
         tags = super().__sklearn_tags__()
diff --git a/imblearn/metrics/tests/test_classification.py b/imblearn/metrics/tests/test_classification.py
index 26d6be0ad..4dbd18e06 100644
--- a/imblearn/metrics/tests/test_classification.py
+++ b/imblearn/metrics/tests/test_classification.py
@@ -454,7 +454,7 @@ def test_iba_error_y_score_prob_error(score_loss):
     y_true, y_pred, _ = make_prediction(binary=True)
 
     aps = make_index_balanced_accuracy(alpha=0.5, squared=True)(score_loss)
-    with pytest.raises(AttributeError):
+    with pytest.raises((AttributeError, TypeError)):
         aps(y_true, y_pred)
 
 
diff --git a/imblearn/over_sampling/_smote/base.py b/imblearn/over_sampling/_smote/base.py
index 7a6abbd2d..c008a95e6 100644
--- a/imblearn/over_sampling/_smote/base.py
+++ b/imblearn/over_sampling/_smote/base.py
@@ -981,6 +981,5 @@ def _more_tags(self):
 
     def __sklearn_tags__(self):
         tags = super().__sklearn_tags__()
-        tags.input_tags.sparse = False
         tags.input_tags.string = True
         return tags
diff --git a/imblearn/tests/test_pipeline.py b/imblearn/tests/test_pipeline.py
index b9c6f9df3..d017b4e86 100644
--- a/imblearn/tests/test_pipeline.py
+++ b/imblearn/tests/test_pipeline.py
@@ -39,7 +39,7 @@
 from imblearn.pipeline import Pipeline, make_pipeline
 from imblearn.under_sampling import EditedNearestNeighbours as ENN
 from imblearn.under_sampling import RandomUnderSampler
-from imblearn.utils._sklearn_compat import sklearn_version
+from imblearn.utils._sklearn_compat import Tags, sklearn_version
 from imblearn.utils.estimator_checks import check_param_validation
 
 JUNK_FOOD_DOCS = (
@@ -61,6 +61,9 @@ def __init__(self, a=None, b=None):
         self.a = a
         self.b = b
 
+    def __sklearn_tags__(self):
+        return Tags()
+
 
 class NoTrans(NoFit):
     def fit(self, X, y):