diff --git a/imblearn/over_sampling/adasyn.py b/imblearn/over_sampling/adasyn.py
index ea99efaac..98fe19a38 100644
--- a/imblearn/over_sampling/adasyn.py
+++ b/imblearn/over_sampling/adasyn.py
@@ -161,6 +161,11 @@ def _sample(self, X, y):
             ratio_nn /= np.sum(ratio_nn)
             n_samples_generate = np.rint(ratio_nn * n_samples).astype(int)
 
+            # the nearest neighbors need to be fitted only on the current class
+            # to find the class NN to generate new samples
+            self.nn_.fit(X_class)
+            _, nn_index = self.nn_.kneighbors(X_class)
+
             x_class_gen = []
             for x_i, x_i_nn, num_sample_i in zip(X_class, nn_index,
                                                  n_samples_generate):
diff --git a/imblearn/over_sampling/tests/test_adasyn.py b/imblearn/over_sampling/tests/test_adasyn.py
index 81fdd6261..5eacb6cac 100644
--- a/imblearn/over_sampling/tests/test_adasyn.py
+++ b/imblearn/over_sampling/tests/test_adasyn.py
@@ -42,18 +42,30 @@ def test_ada_fit():
 def test_ada_fit_sample():
     ada = ADASYN(random_state=RND_SEED)
     X_resampled, y_resampled = ada.fit_sample(X, Y)
-    X_gt = np.array([[0.11622591, -0.0317206], [0.77481731, 0.60935141],
-                     [1.25192108, -0.22367336], [0.53366841, -0.30312976],
-                     [1.52091956, -0.49283504], [-0.28162401, -2.10400981],
-                     [0.83680821, 1.72827342], [0.3084254, 0.33299982],
-                     [0.70472253, -0.73309052], [0.28893132, -0.38761769],
-                     [1.15514042, 0.0129463], [0.88407872, 0.35454207],
-                     [1.31301027, -0.92648734], [-1.11515198, -0.93689695],
-                     [-0.18410027, -0.45194484], [0.9281014, 0.53085498],
-                     [-0.14374509, 0.27370049], [-0.41635887, -0.38299653],
-                     [0.08711622, 0.93259929], [1.70580611, -0.11219234],
-                     [-0.06182085, -0.28084828], [0.38614986, -0.35405599],
-                     [0.39635544, 0.33629036], [-0.24027923, 0.04116021]])
+    X_gt = np.array([[0.11622591, -0.0317206],
+                     [0.77481731, 0.60935141],
+                     [1.25192108, -0.22367336],
+                     [0.53366841, -0.30312976],
+                     [1.52091956, -0.49283504],
+                     [-0.28162401, -2.10400981],
+                     [0.83680821, 1.72827342],
+                     [0.3084254, 0.33299982],
+                     [0.70472253, -0.73309052],
+                     [0.28893132, -0.38761769],
+                     [1.15514042, 0.0129463],
+                     [0.88407872, 0.35454207],
+                     [1.31301027, -0.92648734],
+                     [-1.11515198, -0.93689695],
+                     [-0.18410027, -0.45194484],
+                     [0.9281014, 0.53085498],
+                     [-0.14374509, 0.27370049],
+                     [-0.41635887, -0.38299653],
+                     [0.08711622, 0.93259929],
+                     [1.70580611, -0.11219234],
+                     [0.36370445, -0.19262406],
+                     [0.28204936, -0.13953426],
+                     [0.39635544, 0.33629036],
+                     [0.35301481, 0.25795516]])
     y_gt = np.array([
         0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0
     ])
@@ -65,16 +77,26 @@ def test_ada_fit_sample_half():
     ratio = 0.8
     ada = ADASYN(ratio=ratio, random_state=RND_SEED)
     X_resampled, y_resampled = ada.fit_sample(X, Y)
-    X_gt = np.array([[0.11622591, -0.0317206], [0.77481731, 0.60935141],
-                     [1.25192108, -0.22367336], [0.53366841, -0.30312976],
-                     [1.52091956, -0.49283504], [-0.28162401, -2.10400981],
-                     [0.83680821, 1.72827342], [0.3084254, 0.33299982],
-                     [0.70472253, -0.73309052], [0.28893132, -0.38761769],
-                     [1.15514042, 0.0129463], [0.88407872, 0.35454207],
-                     [1.31301027, -0.92648734], [-1.11515198, -0.93689695],
-                     [-0.18410027, -0.45194484], [0.9281014, 0.53085498],
-                     [-0.14374509, 0.27370049], [-0.41635887, -0.38299653],
-                     [0.08711622, 0.93259929], [1.70580611, -0.11219234]])
+    X_gt = np.array([[0.11622591, -0.0317206],
+                     [0.77481731, 0.60935141],
+                     [1.25192108, -0.22367336],
+                     [0.53366841, -0.30312976],
+                     [1.52091956, -0.49283504],
+                     [-0.28162401, -2.10400981],
+                     [0.83680821, 1.72827342],
+                     [0.3084254, 0.33299982],
+                     [0.70472253, -0.73309052],
+                     [0.28893132, -0.38761769],
+                     [1.15514042, 0.0129463],
+                     [0.88407872, 0.35454207],
+                     [1.31301027, -0.92648734],
+                     [-1.11515198, -0.93689695],
+                     [-0.18410027, -0.45194484],
+                     [0.9281014, 0.53085498],
+                     [-0.14374509, 0.27370049],
+                     [-0.41635887, -0.38299653],
+                     [0.08711622, 0.93259929],
+                     [1.70580611, -0.11219234]])
     y_gt = np.array(
         [0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0])
     assert_allclose(X_resampled, X_gt, rtol=R_TOL)
@@ -85,18 +107,30 @@ def test_ada_fit_sample_nn_obj():
     nn = NearestNeighbors(n_neighbors=6)
     ada = ADASYN(random_state=RND_SEED, n_neighbors=nn)
     X_resampled, y_resampled = ada.fit_sample(X, Y)
-    X_gt = np.array([[0.11622591, -0.0317206], [0.77481731, 0.60935141],
-                     [1.25192108, -0.22367336], [0.53366841, -0.30312976],
-                     [1.52091956, -0.49283504], [-0.28162401, -2.10400981],
-                     [0.83680821, 1.72827342], [0.3084254, 0.33299982],
-                     [0.70472253, -0.73309052], [0.28893132, -0.38761769],
-                     [1.15514042, 0.0129463], [0.88407872, 0.35454207],
-                     [1.31301027, -0.92648734], [-1.11515198, -0.93689695],
-                     [-0.18410027, -0.45194484], [0.9281014, 0.53085498],
-                     [-0.14374509, 0.27370049], [-0.41635887, -0.38299653],
-                     [0.08711622, 0.93259929], [1.70580611, -0.11219234],
-                     [-0.06182085, -0.28084828], [0.38614986, -0.35405599],
-                     [0.39635544, 0.33629036], [-0.24027923, 0.04116021]])
+    X_gt = np.array([[0.11622591, -0.0317206],
+                     [0.77481731, 0.60935141],
+                     [1.25192108, -0.22367336],
+                     [0.53366841, -0.30312976],
+                     [1.52091956, -0.49283504],
+                     [-0.28162401, -2.10400981],
+                     [0.83680821, 1.72827342],
+                     [0.3084254, 0.33299982],
+                     [0.70472253, -0.73309052],
+                     [0.28893132, -0.38761769],
+                     [1.15514042, 0.0129463],
+                     [0.88407872, 0.35454207],
+                     [1.31301027, -0.92648734],
+                     [-1.11515198, -0.93689695],
+                     [-0.18410027, -0.45194484],
+                     [0.9281014, 0.53085498],
+                     [-0.14374509, 0.27370049],
+                     [-0.41635887, -0.38299653],
+                     [0.08711622, 0.93259929],
+                     [1.70580611, -0.11219234],
+                     [0.36370445, -0.19262406],
+                     [0.28204936, -0.13953426],
+                     [0.39635544, 0.33629036],
+                     [0.35301481, 0.25795516]])
     y_gt = np.array([
         0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0
     ])