diff --git a/bayesnet/ensembles/BoostAODE.cc b/bayesnet/ensembles/BoostAODE.cc
index fba10f3..f9c2205 100644
--- a/bayesnet/ensembles/BoostAODE.cc
+++ b/bayesnet/ensembles/BoostAODE.cc
@@ -22,7 +22,7 @@ namespace bayesnet {
     BoostAODE::BoostAODE(bool predict_voting) : Ensemble(predict_voting)
     {
         validHyperparameters = {
-            "repeatSparent", "maxModels", "order", "convergence", "threshold",
+            "maxModels", "order", "convergence", "threshold",
             "select_features", "tolerance", "predict_voting", "predict_single"
         };
 
@@ -63,10 +63,6 @@ namespace bayesnet {
     void BoostAODE::setHyperparameters(const nlohmann::json& hyperparameters_)
     {
         auto hyperparameters = hyperparameters_;
-        if (hyperparameters.contains("repeatSparent")) {
-            repeatSparent = hyperparameters["repeatSparent"];
-            hyperparameters.erase("repeatSparent");
-        }
         if (hyperparameters.contains("maxModels")) {
             maxModels = hyperparameters["maxModels"];
             hyperparameters.erase("maxModels");
@@ -230,22 +226,15 @@ namespace bayesnet {
             if (order_algorithm == Orders.RAND) {
                 std::shuffle(featureSelection.begin(), featureSelection.end(), g);
             }
-            auto feature = featureSelection[0];
-            if (!repeatSparent || featuresUsed.size() < featureSelection.size()) {
-                bool used = true;
-                for (const auto& feat : featureSelection) {
-                    if (std::find(featuresUsed.begin(), featuresUsed.end(), feat) != featuresUsed.end()) {
-                        continue;
-                    }
-                    used = false;
-                    feature = feat;
-                    break;
-                }
-                if (used) {
-                    exitCondition = true;
-                    continue;
-                }
+            // Remove used features
+            featureSelection.erase(remove_if(begin(featureSelection), end(featureSelection), [&](auto x)
+                { return find(begin(featuresUsed), end(featuresUsed), x) != end(featuresUsed);}),
+                end(featureSelection)
+            );
+            if (featureSelection.empty()) {
+                break;
             }
+            auto feature = featureSelection[0];
             std::unique_ptr<Classifier> model;
             model = std::make_unique<SPODE>(feature);
             model->fit(dataset, features, className, states, weights_);