# 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
**ELMo** & **HoC** & **without** & **RS** & **LC** & **IDD** & **Margin** & **MRD** \\ \hline \multirow{3}{*}{SVM} & F1 & 85.76 & 84.51 & 90.51 & 90.78 & 90.62 & 90.73 \\ \cline{2-8}  & F1 & 84.75 & 82.58 & 84.84 & 84.21 & 84.84 & 82.73 \\ \hline \multirow{3}{*}{KNN} & F1 & 84.06 & 82.95 & 81.86 & 82.19 & 83.91 & 81.64 \\ \cline{2-8}  & F1 & 88.31 & 87.10 & 88.08 & 87.94 & 88.12 & 87.95 \\ \hline \multirow{3}{*}{Random forest} & F1 & 78.45 & 78.56 & 79.94 & 74.27 & 79.94 & 79.92 \\ \cline{2-8}  & F1 & 98.10 & 90.95 & 91.49 & 94.81 & 93.94 & 93.09 \\ \hline \multirow{3}{*}{CatBoost} & F1 & 86.61 & 87.16 & 87.16 & 86.69 & 87.39 & 88.01 \\ \cline{2-8}  & F1 & 88.23 & 90.20 & 88.65 & 90.62 & 90.66 & 89.94 \\ \hline \multirow{3}{*}{Random forest} & Accuracy & 80.82 & 79.92 & 80.43 & 80.32 & 81.41 & 80.85 \\ \cline{2-8}  & F1 & 85.31 & 84.44 & 86.21 & 85.10 & 87.06 & 86.15 \\ \hline \multirow{3}{*}{AdaBoost} & Accuracy & 77.56 & 78.01 & 79.76 & 78.15 & 80.99 & 80.16 \\ \cline{2-8}  & F1 & 81.32 & 82.40 & 83.53 & 82.45 & 86.53 & 85.32 \\ \hline \multirow{3}{*}{CatBoost} & Accuracy & 80.45 & 82.42 & 81.43 & 82.97 & 83.02 & 82.90 \\ \cline{2-8}  & F1 & 87.34 & 88.71 & 89.54 & 90.35 & 90.42 & 89.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of results with and without AL methods (ELMo) for HoC dataset. * CatBoost performs better than others in most situations after we summarise all results tables. * In general, LC and margin have better performance than other query strategies after we summarise all result tables. * Overall, AL performance is better; therefore, AL is more recommended than passive learning. In addition to the above results, we also notice that CatBoost always performs stably in every situation where other classifiers somehow have some bad performance. The judgement of LC and margin performance is challenging since they still have similar performance in almost all cases. For the first results, CatBoost, as described in the methodology, is part of the gradient boost based on DT. This structure gives CatBoost the ability to get more chances to recover the errors during the implementation of the entire CatBoost structure since the later tree will fix the error that occurred by the previous tree. At the same time, the CatBoost boosting scheme is modified to be more efficient than other gradient boost algorithms, such as XGBoost, which gives CatBoost stability when changing the hyperparameters, especially with extensive data. All these advantages make CatBoost, overall, have a better ability to perform better in our study. Besides other algorithms such as AdaBoost, SVM is very sensitive with the correlation between data and data, which lead them to make more mistakes during the training and prediction than other algorithms. For the second result, both LC and margin belong to uncertainty sampling, which calculates the uncertainty between data to measure the value of the word to decide the query order. Therefore, we can consider these two methods as a similar scheme used for AL. Then, uncertainty sampling was invented to reduce classification errors, making them more able to reduce classification errors than other query strategies, which is also what our study aims for. At the same time, IDD and MRD focused more on one word to decide the values. This could be better than uncertainty sampling with efficiently pre-computed; however, we cannot develop an efficiently pre-computed IDD and MRD algorithm to test the performance due to the tight time for implementation. This lead IDD and MRD cannot perform better than LC and Margin. For the third result, the main reason why we can achieve better performance since with fewer data trained by using AL than using passive learning is the unbalance of the dataset. More data does not mean more accuracy for text classification. There exist iterations for the classification, even with all valuable data. In this time, insufficient data can immediately cause errors and reduce the accuracy of the classification.