## 3 Results
Additionally, we compare our approach with a Decision Tree classifier (C4.5) and a Support Vector Machine (SVM), two state-of-the-art classification algorithms. In order to perform these comparisons, we applied the Weka software package [38] and tested the two classifiers on the BRCA dataset only with ten different randomly generated training (80%) and test (20%) sets. We summarised the achieved results with the SVM and the Decision Tree classifiers in Tables 7 and 8, respectively,( by varying their parameters. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & \begin{tabular}{c} **HD Vectors** \\ **Dimensionality** \\ \end{tabular} & **HD-Levels** & \begin{tabular}{c} **Retraining** \\ **Iterations** \\ \end{tabular} & \begin{tabular}{c} **Training** \\ **Time (h)** \\ \end{tabular} & \
\begin{tabular}{c} **Classification** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} \\ \hline \multirow{9}{*}{BRCA} & 9000 & 900 & 100 & 10.53 & 6 & 56.11 \\  & 9000 & 600 & 25 & 3.08 & 1 & 54.44 \\  & 9000 & 500 & 100 & 3.07 & 6 & 57.78 \\  & 7000 & 900 & 40 & 13.85 & 2 & 56.67 \\  & 7000 & 700 & 52 & 5.08 & 3 & 57.78 \\  & 7000 & 500 & 82 & 2.94 & 4 & 59.44 \\  & 5000 & 900 & 5 & 3.51 & \textless{}1 & 51.11 \\  & 5000 & 500 & 7 & 8.30 & \textless{}1 & 48.89 \\  & 3000 & 900 & 84 & 2.79 & 3 & 58.33 \\  & 3000 & 500 & 85 & 3.34 & 3 & 61.11 \\  & **1000** & **900** & **3** & **2.33** & **\textless{}1** & **88.89** \\  & 1000 & 500 & 65 & 11.41 & 2 & 60.00 \\ \hline \multirow{9}{*}{KIRP} & 9000 & 900 & 94 & 3.67 & 2 & 64.06 \\  & 9000 & 600 & 12 & 1.07 & \textless{}1 & 57.81 \\  & 9000 & 500 & 29 & 1.05 & \textless{}1 & 59.38 \\  & 7000 & 900 & 100 & 1.00 & 1 & 62.50 \\  & 7000 & 700 & 69 & 1.00 & 1 & 62.50 \\ KIRP & 7000 & 500 & 2 & 0.98 & \textless{}1 & 60.94 \\  & 5000 & 900 & 25 & 0.94 & \textless{}1 & 64.06 \\  & 5000 & 500 & 38 & 0.93 & \textless{}1 & 62.50 \\  & 3000 & 900 & 94 & 0.82 & 1 & 62.50 \\  & 3000 & 500 & 8 & 0.82 & \textless{}1 & 60.94 \\  & **1000** & **900** & **3** & **0.78** & \textless{}1** & **85.94** \\  & 1000 & 500 & 24 & 0.77 & \textless{}1 & 59.38 \\ \hline \multirow{9}{*}{THCA} & 9000 & 900 & 32 & 2.00 & 1 & 54.39 \\  & 9000 & 600 & 18 & 2.10 & \textless{}1 & 57.89 \\ \cline{1-1}  & 9000 & 500 & 38 & 2.07 & 1 & 59.65 \\ \cline{1-1}  & 7000 & 900 & 100 & 1.91 & 3 & 64.04 \\ \cline{1-1}  & 7000 & 700 & 50 & 1.95 & 1 & 61.40 \\ \cline{1-1}  & 7000 & 500 & 43 & 2.39 & 1 & 61.40 \\ \cline{1-1}  & 5000 & 900 & 22 & 1.71 & \textless{}1 & 56.14 \\ \cline{1-1}  & 5000 & 500 & 26 & 1.69 & \textless{}1 & 56.14 \\ \cline{1-1}  & **3000** & **900** & **5** & **1.87** & **\textless{}1** & **100.0** \\ \cline{1-1}  & 3000 & 500 & 61 & 1.62 & 1 & 62.28 \\ \cline{1-1}  & **1000** & **900** & **3** & **1.47** & **\textless{}1** & **90.35** \\ \cline{1-1}  & **1000** & **500** & **6** & **4.95** & **\textless{}1** & **99.12** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Benchmarks of the HD classification algorithm performances with different configurations of HD vectors dimensionality and HD-levels with a maximum amount of 100 retraining iterations. The best result achieved by the HD-classifier after the number of retraining iterations is shown. The configurations that led to a classification accuracy greater than 80% have been highlighted. It is worth noting that all the proposed comparisons have been performed in multithreading. We tried to also repeat these experiments by running the sequential implementation of the classification algorithms, with no results, even after days of computation. It is also worth to note that we were able to perform our analyses with the HD classifier on a consumer laptop with 1.2 GHz (Intel Core M) CPU and 8 GB of RAM in single thread, proving that the HD approach can handle massive datasets with a very limited amount of computational resources.