diff --git a/multiview_platform/MonoMultiViewClassifiers/ExecClassif.py b/multiview_platform/MonoMultiViewClassifiers/ExecClassif.py
index ed9d7849f1e208ff8d7d6c23ef8aadbf077c0887..64c9f085ef20f139f1ce9c4f1f3f96fdba16e452 100644
--- a/multiview_platform/MonoMultiViewClassifiers/ExecClassif.py
+++ b/multiview_platform/MonoMultiViewClassifiers/ExecClassif.py
@@ -459,8 +459,10 @@ def execClassif(arguments):
 
     getDatabase = execution.getDatabaseFunction(args.name,args.type)
 
-    DATASET, LABELS_DICTIONARY = getDatabase(args.views, args.pathF, args.name, args.CL_nbClass,
+
+    DATASET, LABELS_DICTIONARY, datasetname = getDatabase(args.views, args.pathF, args.name, args.CL_nbClass,
                                              args.CL_classes, randomState, args.full, args.add_noise, args.noise_std)
+    args.name = datasetname
 
     splits = execution.genSplits(DATASET.get("Labels").value, args.CL_split, statsIterRandomStates)
 
diff --git a/multiview_platform/MonoMultiViewClassifiers/utils/GetMultiviewDb.py b/multiview_platform/MonoMultiViewClassifiers/utils/GetMultiviewDb.py
index 0086ae5cf4babc132ca10a0fcae294781bef1897..1143c2ea080127ea407cf6e3a1686d64b56c55e5 100644
--- a/multiview_platform/MonoMultiViewClassifiers/utils/GetMultiviewDb.py
+++ b/multiview_platform/MonoMultiViewClassifiers/utils/GetMultiviewDb.py
@@ -125,7 +125,7 @@ def getPlausibleDBhdf5(features, pathF, name, NB_CLASS=3, LABELS_NAME="", random
         datasetFile.close()
         datasetFile = h5py.File(pathF + "Plausible.hdf5", "r")
         LABELS_DICTIONARY = {0: "No", 1: "Yes", 2:"Maybe"}
-        return datasetFile, LABELS_DICTIONARY
+        return datasetFile, LABELS_DICTIONARY, "Plausible"
 
 
 # def getFakeDBhdf5(features, pathF, name, NB_CLASS, LABELS_NAME, randomState):
@@ -322,10 +322,10 @@ def getClassicDBhdf5(views, pathF, nameDB, NB_CLASS, askedLabelsNames, randomSta
                                 enumerate(datasetFile.get("Labels").attrs["names"]))
 
     if add_noise:
-        datasetFile = add_gaussian_noise(datasetFile, randomState, pathF, dataset_name, noise_std)
+        datasetFile, dataset_name = add_gaussian_noise(datasetFile, randomState, pathF, dataset_name, noise_std)
     else:
         pass
-    return datasetFile, labelsDictionary
+    return datasetFile, labelsDictionary, dataset_name
 
 
 def add_gaussian_noise(dataset_file, random_state, path_f, dataset_name, noise_std=0.15):
@@ -353,7 +353,7 @@ def add_gaussian_noise(dataset_file, random_state, path_f, dataset_name, noise_s
         noised_data = np.where(noised_data>view_limits[:,1], view_limits[:,1], noised_data)
         noisy_dataset[view_name][...] = noised_data
         final_shape = noised_data.shape
-    return noisy_dataset
+    return noisy_dataset, dataset_name+"_noised"