diff --git a/quapy/functional.py b/quapy/functional.py
index 3a4ebfa..1459a0f 100644
--- a/quapy/functional.py
+++ b/quapy/functional.py
@@ -1,6 +1,6 @@
 import itertools
 from collections import defaultdict
-from typing import Union, Callable
+from typing import Literal, Union, Callable
 
 import scipy
 import numpy as np
@@ -374,4 +374,55 @@ def linear_search(loss, n_classes):
         if min_score is None or score < min_score:
             prev_selected, min_score = prev, score
 
-    return np.asarray([1 - prev_selected, prev_selected])
\ No newline at end of file
+    return np.asarray([1 - prev_selected, prev_selected])
+
+
+def _project_onto_probability_simplex(v: np.ndarray) -> np.ndarray:
+    """Projects a point onto the probability simplex.
+
+    The code is adapted from Mathieu Blondel's BSD-licensed
+    `implementation <https://gist.github.com/mblondel/6f3b7aaad90606b98f71>`_
+    which is accompanying the paper
+
+    Mathieu Blondel, Akinori Fujino, and Naonori Ueda.
+    Large-scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex,
+    ICPR 2014, `URL <http://www.mblondel.org/publications/mblondel-icpr2014.pdf>`_
+
+    :param v: point in n-dimensional space, shape `(n,)`
+    :return: projection of `v` onto (n-1)-dimensional probability simplex, shape `(n,)`
+    """
+    v = np.asarray(v)
+    n = len(v)
+
+    # Sort the values in the descending order
+    u = np.sort(v)[::-1]
+
+    cssv = np.cumsum(u) - 1.0
+    ind = np.arange(1, n + 1)
+    cond = u - cssv / ind > 0
+    rho = ind[cond][-1]
+    theta = cssv[cond][-1] / float(rho)
+    return np.maximum(v - theta, 0)
+
+
+
+def clip_prevalence(p: np.ndarray, method: Literal[None, "none", "clip", "project"]) -> np.ndarray:
+    """
+    Clips the proportions vector `p` so that it is a valid probability distribution.
+
+    :param p: the proportions vector to be clipped, shape `(n_classes,)`
+    :param method: the method to use for normalization.
+        If `None` or `"none"`, no normalization is performed.
+        If `"clip"`, the values are clipped to the range [0,1] and normalized, so they sum up to 1.
+        If `"project"`, the values are projected onto the probability simplex.
+    :return: the normalized prevalence vector, shape `(n_classes,)`
+    """
+    if method is None or method == "none":
+        return p
+    elif method == "clip":
+        adjusted = np.clip(p, 0, 1)
+        return adjusted / adjusted.sum()
+    elif method == "project":
+        return _project_onto_probability_simplex(p)
+    else:
+        raise ValueError(f"Method {method} not known.")
diff --git a/quapy/method/aggregative.py b/quapy/method/aggregative.py
index da98358..5ea0473 100644
--- a/quapy/method/aggregative.py
+++ b/quapy/method/aggregative.py
@@ -443,8 +443,7 @@ class ACC(AggregativeCrispQuantifier):
 
             try:
                 adjusted_prevs = np.linalg.solve(A, B)
-                adjusted_prevs = np.clip(adjusted_prevs, 0, 1)
-                adjusted_prevs /= adjusted_prevs.sum()
+                adjusted_prevs = F.clip_prevalence(adjusted_prevs, method="clip")
             except np.linalg.LinAlgError:
                 adjusted_prevs = prevs_estim  # no way to adjust them!