Index: trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/TopMetricFilter.java
===================================================================
--- trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/TopMetricFilter.java	(revision 128)
+++ trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/TopMetricFilter.java	(revision 129)
@@ -21,4 +21,6 @@
 import java.util.TreeSet;
 import java.util.logging.Level;
+import java.util.regex.Matcher;
+import java.util.regex.Pattern;
 import java.util.stream.IntStream;
 
@@ -29,4 +31,5 @@
 
 import de.ugoe.cs.cpdp.util.SortUtils;
+import de.ugoe.cs.cpdp.util.WekaUtils;
 import de.ugoe.cs.util.console.Console;
 import weka.attributeSelection.AttributeSelection;
@@ -96,16 +99,64 @@
         List<Set<Integer>> cfsSets = new LinkedList<>();
         for( Instances traindata : traindataSet ) {
-            AttributeSelection attsel = new AttributeSelection();
-            CfsSubsetEval eval = new CfsSubsetEval();
-            GreedyStepwise search = new GreedyStepwise();
-            search.setSearchBackwards(true);
-            attsel.setEvaluator(eval);
-            attsel.setSearch(search);
-            attsel.SelectAttributes(traindata);
-            Set<Integer> cfsSet = new HashSet<>();
-            for( int attr : attsel.selectedAttributes() ) {
-                cfsSet.add(attr);
-            }
-            cfsSets.add(cfsSet);
+            boolean selectionSuccessful = false;
+            boolean secondAttempt = false;
+            Instances traindataCopy = null;
+            do {
+                try {
+                    if (secondAttempt) {
+                        AttributeSelection attsel = new AttributeSelection();
+                        CfsSubsetEval eval = new CfsSubsetEval();
+                        GreedyStepwise search = new GreedyStepwise();
+                        search.setSearchBackwards(true);
+                        attsel.setEvaluator(eval);
+                        attsel.setSearch(search);
+                        attsel.SelectAttributes(traindataCopy);
+                        Set<Integer> cfsSet = new HashSet<>();
+                        for( int attr : attsel.selectedAttributes() ) {
+                            cfsSet.add(attr);
+                        }
+                        cfsSets.add(cfsSet);
+                        selectionSuccessful = true;
+                    }
+                    else {
+                        AttributeSelection attsel = new AttributeSelection();
+                        CfsSubsetEval eval = new CfsSubsetEval();
+                        GreedyStepwise search = new GreedyStepwise();
+                        search.setSearchBackwards(true);
+                        attsel.setEvaluator(eval);
+                        attsel.setSearch(search);
+                        attsel.SelectAttributes(traindata);
+                        Set<Integer> cfsSet = new HashSet<>();
+                        for( int attr : attsel.selectedAttributes() ) {
+                            cfsSet.add(attr);
+                        }
+                        cfsSets.add(cfsSet);
+                        selectionSuccessful = true;
+                    }
+                }
+                catch (IllegalArgumentException e) {
+                    String regex = "A nominal attribute \\((.*)\\) cannot have duplicate labels.*";
+                    Pattern p = Pattern.compile(regex);
+                    Matcher m = p.matcher(e.getMessage());
+                    if (!m.find()) {
+                        // cannot treat problem, rethrow exception
+                        throw e;
+                    }
+                    String attributeName = m.group(1);
+                    int attrIndex = traindata.attribute(attributeName).index();
+                    if (secondAttempt) {
+                        traindataCopy = WekaUtils.upscaleAttribute(traindataCopy, attrIndex);
+                    }
+                    else {
+                        traindataCopy = WekaUtils.upscaleAttribute(traindata, attrIndex);
+                    }
+                    Console
+                        .traceln(Level.FINE,
+                                 "upscaled attribute " + attributeName + "; restarting training");
+                    secondAttempt = true;
+                    continue;
+                }
+            }
+            while (!selectionSuccessful); // dummy loop for internal continue
         }
         
@@ -143,5 +194,6 @@
         }
         Set<Integer> topkSetIndexSet = new TreeSet<>();
-        for( int j=0; j<bestCoverageIndex; j++ ) {
+        // j<30 ensures that the computational time does not explode since the powerset is 2^n in complexity
+        for( int j=0; j<bestCoverageIndex && j<30 ; j++ ) {
             topkSetIndexSet.add(j);
         }
@@ -157,5 +209,5 @@
                 }
                 for( Set<Integer> cfsSet : cfsSets ) {
-                    currentCoverage += (coverage(combination, cfsSet)/traindataSet.size());
+                    currentCoverage += (coverage(topkCombination, cfsSet)/traindataSet.size());
                 }
                 if( currentCoverage > bestOptCoverage ) {
Index: trunk/CrossPare/src/de/ugoe/cs/cpdp/util/WekaUtils.java
===================================================================
--- trunk/CrossPare/src/de/ugoe/cs/cpdp/util/WekaUtils.java	(revision 128)
+++ trunk/CrossPare/src/de/ugoe/cs/cpdp/util/WekaUtils.java	(revision 129)
@@ -37,4 +37,9 @@
         }
     }
+    
+    /**
+     * Scaling value that moves the decimal point by 5 digets.
+     */
+    public final static double SCALER = 10000.0d;
     
     /**
@@ -151,3 +156,24 @@
         return new DistChar(mean, std, min, max, data.numInstances());
     }
+    
+    /**
+     * <p>
+     * Upscales the value of a single attribute. This is a workaround to get BayesNet running for
+     * all data. Works on a copy of the training data, i.e., leaves the original data untouched.
+     * </p>
+     *
+     * @param traindata
+     *            data from which the attribute is upscaled.
+     * @param attributeIndex
+     *            index of the attribute
+     * @return data with upscaled attribute
+     */
+    public static Instances upscaleAttribute(Instances traindata, int attributeIndex) {
+        Instances traindataCopy = new Instances(traindata);
+        for (int i = 0; i < traindata.size(); i++) {
+            traindataCopy.get(i).setValue(attributeIndex,
+                                          traindata.get(i).value(attributeIndex) * SCALER);
+        }
+        return traindataCopy;
+    }
 }
Index: trunk/CrossPare/src/de/ugoe/cs/cpdp/wekaclassifier/AbstractCODEP.java
===================================================================
--- trunk/CrossPare/src/de/ugoe/cs/cpdp/wekaclassifier/AbstractCODEP.java	(revision 128)
+++ trunk/CrossPare/src/de/ugoe/cs/cpdp/wekaclassifier/AbstractCODEP.java	(revision 129)
@@ -23,4 +23,5 @@
 import java.util.regex.Pattern;
 
+import de.ugoe.cs.cpdp.util.WekaUtils;
 import de.ugoe.cs.util.console.Console;
 import weka.classifiers.AbstractClassifier;
@@ -71,9 +72,4 @@
     private Map<Integer, Integer> upscaleIndex = null;
 
-    /**
-     * Scaling value that moves the decimal point by 5 digets.
-     */
-    private final double SCALER = 10000.0d;
-
     /*
      * (non-Javadoc)
@@ -136,5 +132,5 @@
                     }
                     else {
-                        traindataCopy = upscaleAttribute(traindata, attrIndex);
+                        traindataCopy = WekaUtils.upscaleAttribute(traindata, attrIndex);
                     }
 
@@ -182,5 +178,5 @@
                 // instance value must be upscaled
                 int attrIndex = upscaleIndex.get(j);
-                double upscaledVal = instance.value(attrIndex) * SCALER;
+                double upscaledVal = instance.value(attrIndex) * WekaUtils.SCALER;
                 traindataCopy = new Instances(instance.dataset());
                 instance = new DenseInstance(instance.weight(), instance.toDoubleArray());
@@ -231,25 +227,4 @@
     /**
      * <p>
-     * Upscales the value of a single attribute. This is a workaround to get BayesNet running for
-     * all data. Works on a copy of the training data, i.e., leaves the original data untouched.
-     * </p>
-     *
-     * @param traindata
-     *            data from which the attribute is upscaled.
-     * @param attributeIndex
-     *            index of the attribute
-     * @return data with upscaled attribute
-     */
-    private Instances upscaleAttribute(Instances traindata, int attributeIndex) {
-        Instances traindataCopy = new Instances(traindata);
-        for (int i = 0; i < traindata.size(); i++) {
-            traindataCopy.get(i).setValue(attributeIndex,
-                                          traindata.get(i).value(attributeIndex) * SCALER);
-        }
-        return traindataCopy;
-    }
-
-    /**
-     * <p>
      * Abstract method through which implementing classes define which classifier is used for the
      * CODEP.