# Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble Learning
## 5 Results and Analysis
### Results  
The results for different metrics as discussed above are presented in Table 2. We compared the performance of several classifiers with the proposed ensemble-based approach with or without feature selection. SVM, KNN, logistic, and their ensemble classifiers were used in the experiment, along with a boosting and stacking approach. Different classifiers, such as SVM, KNN, logistic, and decision tree, are used in Ensemble learning, coordinated  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Classifier** & **Name of Hyper-Parameter** \\ \hline SVM & Kernel = fbf \\ KNN & k = 7 \\ Decision Tree & MaxDepth = 4 \\ Logistic & Random State = 1 \\ Random Forest & n-estimators = 50, max-depth = 100, random-state = 15 \\ MLP & No. of units (165, 50), Activation = Sigmoid, Optimizer = Adam, Epochs = 10, Layers = 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyper-Parameters.  
using the sampling and the stacking method. In Table 2, CFS indicates the results with feature selection.  
It is clear by this analysis that Ensemble learning improves significantly the performance metrics, compared to single classifiers. In Figure 3, we compare the results in graphical form for an easier analysis.  
For pattern recognition, a classifier makes its decisions based on what is has learned from training data, adapting its behavior to the structure of data it is able to identify. However, in some cases, different hints may arise from different aspects of the data, and these hints may be conflicting. In such situations, the classification model must make a choice. Ensemble pattern recognition improves classification metrics by using multiple methods of pattern recognition at the same time. Ensemble learning improves parameters with explicit and implicit weighting through domain specialization, resulting in better classification performance metrics. This is especially useful when multiple classifiers capture different patterns in the structure of data. Based on Figure 3, we observe that the Ensemble (Boosting-CFS) approach exhibits a better performance compared to other approaches. In general, models with CFS perform better than their counterparts without CFS. The Ensemble (Stacking-CFS), in fact, is second best.  
Following a cumulative performance analysis of both classes of risky and non-risky blocks of the proposed solution, without feature selection in Table 3 and with feature selection in Table 4, it is clear that both types of blocks are expected in equilibrium.  
Figures 4 and 5 show the results for different evaluation parameters, for the Ensemble Boosting and Stacking models. It can be observed that feature selection influences positively the predictive power. Results, in general suggest that the data have a very complex structure,  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Approaches** & **Accuracy** & **Precision** & **Recall** & **F-Measure** \\ \hline SVM & 0.97 & 0.92 & 0.79 & 0.84 \\ KNN & 0.96 & 0.82 & 0.79 & 0.80 \\ Logistic & 0.93 & 0.71 & 0.79 & 0.75 \\ Decision Tree & 0.96 & 0.85 & 0.83 & 0.83 \\ MLP & 0.96 & 0.83 & 0.61 & 0.65 \\ Ensemble (Boosting) & **0.98** & **0.98** & 0.82 & 0.89 \\ Ensemble (Stacking) & 0.97 & 0.95 & 0.81 & 0.86 \\ Ensemble (Stacking-CFS) & **0.98** & **0.98** & 0.92 & 0.92 \\ Ensemble (Boosting-CFS) & **0.98** & **0.98** & **0.93** & **0.95** \\ Gradient Boosting Classifier & 0.96 & 0.95 & 0.79 & 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification Report.  
Figure 3: Comparison of proposed and existing approaches on Different Performance Metrics.