diff --git a/hiclass/HierarchicalClassifier.py b/hiclass/HierarchicalClassifier.py
index 23e422ab..e36e1e14 100644
--- a/hiclass/HierarchicalClassifier.py
+++ b/hiclass/HierarchicalClassifier.py
@@ -71,6 +71,7 @@ def __init__(
         bert: bool = False,
         classifier_abbreviation: str = "",
         tmp_dir: str = None,
+        warm_start: bool = False,
     ):
         """
         Initialize a local hierarchical classifier.
@@ -99,6 +100,9 @@ def __init__(
         tmp_dir : str, default=None
             Temporary directory to persist local classifiers that are trained. If the job needs to be restarted,
             it will skip the pre-trained local classifier found in the temporary directory.
+        warm_start : bool, default=False
+            When set to true, the hierarchical classifier reuses the solution of the previous call to fit, that is,
+            new classes can be added.
         """
         self.local_classifier = local_classifier
         self.verbose = verbose
@@ -108,6 +112,7 @@ def __init__(
         self.bert = bert
         self.classifier_abbreviation = classifier_abbreviation
         self.tmp_dir = tmp_dir
+        self.warm_start = warm_start
 
     def fit(self, X, y, sample_weight=None):
         """
@@ -155,6 +160,8 @@ def _pre_fit(self, X, y, sample_weight):
         else:
             self.sample_weight_ = None
 
+        self.warm_start_ = self.warm_start
+
         self.y_ = make_leveled(self.y_)
 
         # Create and configure logger
@@ -164,7 +171,7 @@ def _pre_fit(self, X, y, sample_weight):
         # which would generate the prediction a->b->c
         self._disambiguate()
 
-        # Create DAG from self.y_ and store to self.hierarchy_
+        # Create or update DAG from self.y_ and store to self.hierarchy_
         self._create_digraph()
 
         # If user passes edge_list, then export
@@ -229,7 +236,7 @@ def _create_digraph(self):
         self._create_digraph_2d()
 
         if self.y_.ndim > 2:
-            # Unsuported dimension
+            # Unsupported dimension
             self.logger_.error(f"y with {self.y_.ndim} dimensions detected")
             raise ValueError(
                 f"Creating graph from y with {self.y_.ndim} dimensions is not supported"
@@ -250,7 +257,10 @@ def _create_digraph_1d(self):
     def _create_digraph_2d(self):
         if self.y_.ndim == 2:
             # Create max_levels variable
-            self.max_levels_ = self.y_.shape[1]
+            if self.warm_start_:
+                self.max_levels_ = max(self.max_levels_, self.y_.shape[1])
+            else:
+                self.max_levels_ = self.y_.shape[1]
             rows, columns = self.y_.shape
             self.logger_.info(f"Creating digraph from {rows} 2D labels")
             for row in range(rows):
@@ -296,7 +306,10 @@ def _add_artificial_root(self):
         self.logger_.info(f"Detected {len(roots)} roots")
 
         # Add artificial root as predecessor to root(s) detected
-        self.root_ = "hiclass::root"
+        if self.warm_start_:
+            roots.remove(self.root_)
+        else:
+            self.root_ = "hiclass::root"
         for old_root in roots:
             self.hierarchy_.add_edge(self.root_, old_root)
 
diff --git a/hiclass/LocalClassifierPerLevel.py b/hiclass/LocalClassifierPerLevel.py
index 907e61cf..2e41d331 100644
--- a/hiclass/LocalClassifierPerLevel.py
+++ b/hiclass/LocalClassifierPerLevel.py
@@ -53,6 +53,7 @@ def __init__(
         n_jobs: int = 1,
         bert: bool = False,
         tmp_dir: str = None,
+        warm_start: bool = False,
     ):
         """
         Initialize a local classifier per level.
@@ -79,6 +80,9 @@ def __init__(
         tmp_dir : str, default=None
             Temporary directory to persist local classifiers that are trained. If the job needs to be restarted,
             it will skip the pre-trained local classifier found in the temporary directory.
+        warm_start : bool, default=False
+            When set to true, the hierarchical classifier reuses the solution of the previous call to fit, that is,
+            new classes can be added.
         """
         super().__init__(
             local_classifier=local_classifier,
@@ -89,6 +93,7 @@ def __init__(
             classifier_abbreviation="LCPL",
             bert=bert,
             tmp_dir=tmp_dir,
+            warm_start=warm_start,
         )
 
     def fit(self, X, y, sample_weight=None):
@@ -115,6 +120,8 @@ def fit(self, X, y, sample_weight=None):
         # Execute common methods necessary before fitting
         super()._pre_fit(X, y, sample_weight)
 
+        # TODO: add partial_fit here if warm_start=True
+
         # Fit local classifiers in DAG
         super().fit(X, y)
 
diff --git a/hiclass/LocalClassifierPerNode.py b/hiclass/LocalClassifierPerNode.py
index 1382c72e..4b2853c1 100644
--- a/hiclass/LocalClassifierPerNode.py
+++ b/hiclass/LocalClassifierPerNode.py
@@ -48,6 +48,7 @@ def __init__(
         n_jobs: int = 1,
         bert: bool = False,
         tmp_dir: str = None,
+        warm_start: bool = False,
     ):
         """
         Initialize a local classifier per node.
@@ -85,6 +86,9 @@ def __init__(
         tmp_dir : str, default=None
             Temporary directory to persist local classifiers that are trained. If the job needs to be restarted,
             it will skip the pre-trained local classifier found in the temporary directory.
+        warm_start : bool, default=False
+            When set to true, the hierarchical classifier reuses the solution of the previous call to fit, that is,
+            new classes can be added.
         """
         super().__init__(
             local_classifier=local_classifier,
@@ -95,6 +99,7 @@ def __init__(
             classifier_abbreviation="LCPN",
             bert=bert,
             tmp_dir=tmp_dir,
+            warm_start=warm_start,
         )
         self.binary_policy = binary_policy
 
@@ -125,6 +130,8 @@ def fit(self, X, y, sample_weight=None):
         # Initialize policy
         self._initialize_binary_policy()
 
+        # TODO: add partial_fit here if warm_start=True
+
         # Fit local classifiers in DAG
         super().fit(X, y)
 
diff --git a/hiclass/LocalClassifierPerParentNode.py b/hiclass/LocalClassifierPerParentNode.py
index 47f77475..8c507f9c 100644
--- a/hiclass/LocalClassifierPerParentNode.py
+++ b/hiclass/LocalClassifierPerParentNode.py
@@ -46,6 +46,7 @@ def __init__(
         n_jobs: int = 1,
         bert: bool = False,
         tmp_dir: str = None,
+        warm_start: bool = False,
     ):
         """
         Initialize a local classifier per parent node.
@@ -72,6 +73,9 @@ def __init__(
         tmp_dir : str, default=None
             Temporary directory to persist local classifiers that are trained. If the job needs to be restarted,
             it will skip the pre-trained local classifier found in the temporary directory.
+        warm_start : bool, default=False
+            When set to true, the hierarchical classifier reuses the solution of the previous call to fit, that is,
+            new classes can be added.
         """
         super().__init__(
             local_classifier=local_classifier,
@@ -82,6 +86,7 @@ def __init__(
             classifier_abbreviation="LCPPN",
             bert=bert,
             tmp_dir=tmp_dir,
+            warm_start=warm_start,
         )
 
     def fit(self, X, y, sample_weight=None):
@@ -165,6 +170,38 @@ def predict(self, X):
 
         return y
 
+    def partial_fit(self, X, y, sample_weight=None):
+        """
+        Add new parent nodes for the local classifier per parent node.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The training input samples. Internally, its dtype will be converted
+            to ``dtype=np.float32``. If a sparse matrix is provided, it will be
+            converted into a sparse ``csc_matrix``.
+        y : array-like of shape (n_samples, n_levels)
+            The target values, i.e., hierarchical class labels for classification.
+        sample_weight : array-like of shape (n_samples,), default=None
+            Array of weights that are assigned to individual samples.
+            If not provided, then each sample is given unit weight.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
+        """
+        self.warm_start_ = True
+
+        # Execute common methods necessary before fitting
+        super()._pre_fit(X, y, sample_weight)
+
+        # Fit local classifiers in DAG
+        super().fit(X, y)
+
+        # Return the classifier
+        return self
+
     def _predict_remaining_levels(self, X, y):
         for level in range(1, y.shape[1]):
             predecessors = set(y[:, level - 1])
@@ -183,7 +220,8 @@ def _initialize_local_classifiers(self):
         local_classifiers = {}
         nodes = self._get_parents()
         for node in nodes:
-            local_classifiers[node] = {"classifier": deepcopy(self.local_classifier_)}
+            if "classifier" not in self.hierarchy_.nodes[node]:
+                local_classifiers[node] = {"classifier": deepcopy(self.local_classifier_)}
         nx.set_node_attributes(self.hierarchy_, local_classifiers)
 
     def _get_parents(self):
diff --git a/tests/test_HierarchicalClassifier.py b/tests/test_HierarchicalClassifier.py
index 3333cf52..4cf72c0b 100644
--- a/tests/test_HierarchicalClassifier.py
+++ b/tests/test_HierarchicalClassifier.py
@@ -57,6 +57,21 @@ def test_create_digraph_1d(graph_1d):
     assert list(ground_truth.edges) == list(graph_1d.hierarchy_.edges)
 
 
+def test_update_digraph_1d(graph_1d):
+    ground_truth = nx.DiGraph()
+    ground_truth.add_nodes_from(np.array(["a", "b", "c", "d", "e", "f"]))
+    graph_1d._create_digraph()
+    attributes = {}
+    attributes["a"] = {"trained_classifier": "yes"}
+    nx.set_node_attributes(graph_1d.hierarchy_, attributes)
+    graph_1d.y_ = np.array(["a", "b", "c", "d", "e", "f"])
+    graph_1d._create_digraph_1d()
+    assert nx.is_isomorphic(ground_truth, graph_1d.hierarchy_)
+    assert list(ground_truth.nodes) == list(graph_1d.hierarchy_.nodes)
+    assert list(ground_truth.edges) == list(graph_1d.hierarchy_.edges)
+    assert graph_1d.hierarchy_.nodes["a"]["trained_classifier"] == "yes"
+
+
 @pytest.fixture
 def graph_1d_disguised_as_2d():
     classifier = HierarchicalClassifier()
@@ -82,6 +97,8 @@ def digraph_2d():
     classifier.logger_ = logging.getLogger("HC")
     classifier.edge_list = tempfile.TemporaryFile()
     classifier.separator_ = "::HiClass::Separator::"
+    classifier.warm_start_ = True
+    classifier.max_levels_ = 3
     return classifier
 
 
@@ -93,6 +110,31 @@ def test_create_digraph_2d(digraph_2d):
     assert list(ground_truth.edges) == list(digraph_2d.hierarchy_.edges)
 
 
+def test_update_digraph_2d(digraph_2d):
+    ground_truth = nx.DiGraph(
+        [
+            ("a", "b"),
+            ("b", "c"),
+            ("d", "e"),
+            ("e", "f"),
+            ("g", "h"),
+            ("h", "i"),
+            ("i", "j"),
+        ]
+    )
+    digraph_2d._create_digraph()
+    attributes = {}
+    attributes["b"] = {"trained_classifier": "yes"}
+    nx.set_node_attributes(digraph_2d.hierarchy_, attributes)
+    digraph_2d.y_ = np.array([["g", "h", "i", "j"]])
+    digraph_2d._create_digraph_2d()
+    assert nx.is_isomorphic(ground_truth, digraph_2d.hierarchy_)
+    assert list(ground_truth.nodes) == list(digraph_2d.hierarchy_.nodes)
+    assert list(ground_truth.edges) == list(digraph_2d.hierarchy_.edges)
+    assert digraph_2d.hierarchy_.nodes["b"]["trained_classifier"] == "yes"
+    assert digraph_2d.max_levels_ == 4
+
+
 @pytest.fixture
 def digraph_3d():
     classifier = HierarchicalClassifier()
@@ -137,6 +179,7 @@ def digraph_one_root():
     classifier = HierarchicalClassifier()
     classifier.logger_ = logging.getLogger("HC")
     classifier.hierarchy_ = nx.DiGraph([("a", "b"), ("b", "c"), ("c", "d")])
+    classifier.warm_start_ = False
     return classifier
 
 
@@ -155,6 +198,7 @@ def digraph_multiple_roots():
     classifier.X_ = np.array([[1, 2], [3, 4], [5, 6]])
     classifier.y_ = np.array([["a", "b"], ["c", "d"], ["e", "f"]])
     classifier.sample_weight_ = None
+    classifier.warm_start_ = False
     return classifier
 
 
@@ -165,6 +209,17 @@ def test_add_artificial_root_multiple_roots(digraph_multiple_roots):
     assert "hiclass::root" == digraph_multiple_roots.root_
 
 
+def test_add_artificial_new_nodes(digraph_multiple_roots):
+    digraph_multiple_roots._add_artificial_root()
+    digraph_multiple_roots.hierarchy_.add_node("g")
+    digraph_multiple_roots.hierarchy_.add_node("h")
+    digraph_multiple_roots.warm_start_ = True
+    digraph_multiple_roots._add_artificial_root()
+    successors = list(digraph_multiple_roots.hierarchy_.successors("hiclass::root"))
+    assert ["a", "c", "e", "g", "h"] == successors
+    assert "hiclass::root" == digraph_multiple_roots.root_
+
+
 def test_initialize_local_classifiers_2(digraph_multiple_roots):
     digraph_multiple_roots.local_classifier = None
     digraph_multiple_roots._initialize_local_classifiers()
@@ -224,6 +279,7 @@ def test_fit_digraph():
 def test_pre_fit_bert():
     classifier = HierarchicalClassifier()
     classifier.logger_ = logging.getLogger("HC")
+    classifier.warm_start_ = False
     classifier.bert = True
     x = [[0, 1], [2, 3]]
     y = [["a", "b"], ["c", "d"]]