diff --git a/imblearn/over_sampling/_random_over_sampler.py b/imblearn/over_sampling/_random_over_sampler.py
index 6ac116305..986be422a 100644
--- a/imblearn/over_sampling/_random_over_sampler.py
+++ b/imblearn/over_sampling/_random_over_sampler.py
@@ -241,10 +241,7 @@ def _fit_resample(self, X, y):
 
         self.sample_indices_ = np.array(sample_indices)
 
-        if sparse.issparse(X):
-            X_resampled = sparse.vstack(X_resampled, format=X.format)
-        else:
-            X_resampled = np.vstack(X_resampled)
+        X_resampled = np.vstack(X_resampled)
         y_resampled = np.hstack(y_resampled)
 
         return X_resampled, y_resampled
diff --git a/imblearn/under_sampling/_prototype_selection/_random_under_sampler.py b/imblearn/under_sampling/_prototype_selection/_random_under_sampler.py
index 6696069c3..03fe71202 100644
--- a/imblearn/under_sampling/_prototype_selection/_random_under_sampler.py
+++ b/imblearn/under_sampling/_prototype_selection/_random_under_sampler.py
@@ -112,7 +112,7 @@ def _fit_resample(self, X, y):
             if target_class in self.sampling_strategy_.keys():
                 n_samples = self.sampling_strategy_[target_class]
                 index_target_class = random_state.choice(
-                    range(np.count_nonzero(y == target_class)),
+                    range(np.count_nonzero(y != target_class)),
                     size=n_samples,
                     replace=self.replacement,
                 )
@@ -122,7 +122,7 @@ def _fit_resample(self, X, y):
             idx_under = np.concatenate(
                 (
                     idx_under,
-                    np.flatnonzero(y == target_class)[index_target_class],
+                    np.flatnonzero(y != target_class)[index_target_class],
                 ),
                 axis=0,
             )