diff --git a/Code/MonoMutliViewClassifiers/ExecClassif.py b/Code/MonoMutliViewClassifiers/ExecClassif.py
index db1b043222de3eae11261c94ed84a0619ae24885..5815e873648c7afdce32ec32b8445e1f2a7bdf45 100644
--- a/Code/MonoMutliViewClassifiers/ExecClassif.py
+++ b/Code/MonoMutliViewClassifiers/ExecClassif.py
@@ -358,7 +358,6 @@ else:
     classifiersNames = [[result[1][0] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in viewsIndices]
     classifiersConfigs = [[result[1][1] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in viewsIndices]
 monoviewTime = time.time()-dataBaseTime-start
-print benchmark
 if True:
     if benchmark["Multiview"]:
         try:
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SCM.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SCM.py
index 449e4f51525c6235d3b5f69cb195dd85bd18002f..9a89cf315d6483b07de7427a6e57e967fc66d14f 100644
--- a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SCM.py
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SCM.py
@@ -32,11 +32,12 @@ def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
         attributeClassification = kwargs["attributeClassification"]
         binaryAttributes = kwargs["binaryAttributes"]
     except:
-        attributeClassification, binaryAttributes, dsetFile = transformData(DATASET)
+        attributeClassification, binaryAttributes, dsetFile, name = transformData(DATASET)
     classifier = pyscm.scm.SetCoveringMachine(p=p, max_attributes=max_attrtibutes, model_type=model_type, verbose=False)
     classifier.fit(binaryAttributes, CLASS_LABELS, X=None, attribute_classifications=attributeClassification, iteration_callback=None)
     try:
         dsetFile.close()
+        os.remove(name)
     except:
         pass
     return classifier
@@ -133,7 +134,7 @@ def transformData(dataArray):
         dsetFile = h5py.File(name, "r")
         packedDataset = dsetFile.get("temp_scm")
         attributeClassification = BaptisteRuleClassifications(packedDataset, nbExamples)
-        return attributeClassification, binaryAttributes, dsetFile
+        return attributeClassification, binaryAttributes, dsetFile, name
 
 
 def isBinary(dataset):