# A Comparative Analysis of Active Learning for Biomedical Text Mining
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**TF-IDF** & **ChemProt** & **without** & **RS** & **LC** & **IDD** & **Margin** & **MRD** \\ \hline \multirow{3}{*}{SVM} & Accuracy & 77.40 & 79.30 & 81.88 & 81.44 & 81.79 & 79.12 \\  & F1 & 80.83 & 86.21 & 86.9 & 87 & 86.71 & 85.45 \\ \cline{2-7}  & Accuracy & 79.57 & 79.59 & 83.04 & 82.16 & 83.01 & 79.61 \\ \cline{2-7}  & F1 & 87.7 & 88.64 & 90.7 & 89.25 & 90.71 & 88.56 \\ \cline{2-7}  & Accuracy & 60.68 & 65.68 & 68.84 & 67.45 & 69.87 & 58.57 \\ \cline{2-7}  & F1 & 57.31 & 64.42 & 66.78 & 64.91 & 68.72 & 54.43 \\ \cline{2-7}  & Accuracy & 78.92 & 78.46 & 78.99 & 78.81 & 79.11 & 78.91 \\ \cline{2-7}  & F1 & 84.36 & 83.38 & 83.82 & 84.26 & 83.86 & 84.64 \\ \cline{2-7}  & Accuracy & 78.85 & 78.32 & 78.55 & 78.5 & 78.6 & 78.58 \\ \cline{2-7}  & F1 & 83.81 & 84.28 & 83.04 & 84.42 & 83.49 & 84.39 \\ \hline \multirow{3}{*}{AdaBoost} & Accuracy & 76.46 & 79.38 & 77.69 & 77.83 & 75.51 & 78.46 \\ \cline{2-7}  & F1 & 82.77 & 86.63 & 82.79 & 85.41 & 81.22 & 86.62 \\ \hline \multirow{3}{*}{CatBoost} & Accuracy & 78.92 & 78.81 & 79.80 & 78.80 & 79.10 & 79.20 \\ \cline{2-7}  & F1 & 84.36 & 83.63 & 85.80 & 84.90 & 84.20 & 84.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of results with and without AL methods (TF-IDF) for ChemProt dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**FastText** & **DDI** & **without** & **RS** & **LC** & **IDD** & **Margin** & **MRD** \\ \hline \multirow{3}{*}{SVM} & Accuracy & 83.13 & 82.59 & 81.79 & 81.51 & 81.54 & 72.97 \\ \cline{2-7}  & F1 & 90.28 & 88.21 & 85.22 & 86.12 & 85.24 & 71.44 \\ \hline \multirow{3}{*}{NB} & Accuracy & 83.01 & 82.93 & 83.15 & 83.01 & 82.99 & 83.02 \\ \cline{2-7}  & F1 & 90.71 & 90.48 & 90.13 & 90.71 & 90.21 & 90.27 \\ \hline \multirow{3}{*}{KNN} & Accuracy & 76.46 & 73.75 & 73.35 & 78.31 & 73.35 & 73.12 \\ \cline{2-7}  & F1 & 77.53 & 74.81 & 73.59 & 81.54 & 73.59 & 73.09 \\ \hline \multirow{3}{*}{XGBoost} & Accuracy & 83.41 & 82.72 & 83.54 & 82.55 & 83.47 & 83.35 \\ \cline{2-7}  & F1 & 89.99 & 89.67 & 89.83 & 89.11 & 90.30 & 89.54 \\ \hline \multirow{3}{*}{Random forest} & Accuracy & 77.45 & 81.53 & 81.21 & 77.69 & 79.97 & 80.66 \\ \cline{2-7}  & F1 & 79.60 & 86.63 & 85.91 & 80.34 & 83.89 & 84.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of results with and without AL methods (FastText) for DDI dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**FastText** & **HoC** & **without** & **RS** & **LC** & **IDD** & **Margin** & **MRD** \\ \hline \multirow{3}{*}{SVM} & F1 & 43.01 & 57.97 & 33.25 & 34.34 & 36.48 & 40.52 \\ \cline{2-7}  & F1 & 20.86 & 36.45 & 31.06 & 29.41 & 41.37 & 27.19 \\ \hline \multirow{3}{*}{KNN} & F1 & 43.43 & 40.04 & 36.89 & 39.72 & 39.09 & 32.64 \\ \cline{2-7}  & F1 & 41.04 & 45.61 & 41.75 & 40.09 & 49.05 & 27.77 \\ \hline \multirow{3}{*}{Random forest} & F1 & 23.47 & 28.08 & 22.87 & 26.68 & 23.95 & 22.04 \\ \cline{2-7}  & F1 & 36.92 & 32.71 & 37.75 & 40.23 & 45.01 & 30.39 \\ \hline \multirow{3}{*}{CatBoost} & F1 & 35.82 & 32.70 & 35.47 & 40.27 & 37.23 & 28.30 \\ \cline{2-7}  & F1 & 89.99 & 89.67 & 89.83 & 89.11 & 90.30 & 89.54 \\ \hline \multirow{3}{*}{Random forest} & Accuracy & 77.45 & 81.53 & 81.21 & 77.69 & 79.97 & 80.66 \\ \cline{2-7}  & F1 & 79.60 & 86.63 & 85.91 & 80.34 & 83.89 & 84.96 \\ \hline \multirow{3}{*}{AdaBoost} & Accuracy & 70.58 & 66.08 & 82.47 & 90.61 & 78.07 & 78.84 \\ \cline{2-7}  & F1 & 70.25 & 63.24 & 89.71 & 82.94 & 82.09 & 83.43 \\ \hline \multirow{3}{*}{CatBoost} & Accuracy & 81.17 & 81.27 & 82.48 & 81.46 & 82.17 & 82.57 \\ \cline{2-7}  & F1 & 85.24 & 86.40 & 88.36 & 87.35 & 87.95 & 88.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of results with and without AL methods (FastText) for HoC dataset.