# Active Learning for Biomedical Article Classification with Bag of Words and _FastText_ Embeddings
## 3 Results
### Active Learning Predictive Performance: Aggregated
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multicolumn{1}{c}{(a) uncertainty sampling} & \multicolumn{6}{c}{(b) diversity sampling} \\ \hline  & SVM-BOW & SVM-FT & RF-BOW & RF-FT & SVM-BOW & SVM-FT & RF-BOW & RF-FT \\ \hline
5\% & 0.19 & 0.28 & 0.33 & 0.33 & 5\% & 0.19 & 0.30 & 0.33 & 0.33 \\
10\% & 0.27 & 0.40 & 0.43 & 0.41 & 10\% & 0.24 & 0.35 & 0.36 & 0.36 \\
25\% & 0.38 & 0.49 & 0.50 & 0.49 & 25\% & 0.31 & 0.42 & 0.41 & 0.41 \\
33\% & 0.40 & 0.51 & 0.52 & 0.50 & 33\% & 0.34 & 0.45 & 0.43 & 0.43 \\
50\% & 0.45 & 0.53 & 0.53 & 0.53 & 50\% & 0.40 & 0.48 & 0.47 & 0.47 \\
66\% & 0.47 & 0.53 & 0.54 & 0.53 & 66\% & 0.43 & 0.51 & 0.49 & 0.49 \\
75\% & 0.48 & 0.54 & 0.54 & 0.53 & 75\% & 0.45 & 0.52 & 0.51 & 0.51 \\
100\% & 0.49 & 0.54 & 0.55 & 0.53 & 100\% & 0.49 & 0.54 & 0.55 & 0.53 \\ \hline \multicolumn{1}{c}{(c) minority sampling} & \multicolumn{6}{c}{(d) threshold sampling} \\ \hline  & SVM-BOW & SVM-FT & RF-BOW & RF-FT & SVM-BOW & SVM-FT & RF-BOW & RF-FT \\ \hline
5\% & 0.19 & 0.29 & 0.33 & 0.34 & 5\% & 0.19 & 0.28 & 0.33 & 0.33 \\
10\% & 0.28 & 0.40 & 0.42 & 0.39 & 10\% & 0.26 & 0.38 & 0.44 & 0.41 \\
25\% & 0.36 & 0.48 & 0.49 & 0.48 & 25\% & 0.37 & 0.48 & 0.50 & 0.48 \\
33\% & 0.40 & 0.50 & 0.51 & 0.50 & 33\% & 0.40 & 0.50 & 0.51 & 0.50 \\
50\% & 0.44 & 0.53 & 0.53 & 0.53 & 50\% & 0.44 & 0.52 & 0.52 & 0.52 \\
66\% & 0.47 & 0.54 & 0.54 & 0.53 & 66\% & 0.47 & 0.53 & 0.54 & 0.52 \\
75\% & 0.48 & 0.54 & 0.55 & 0.53 & 75\% & 0.48 & 0.53 & 0.54 & 0.52 \\
100\% & 0.49 & 0.54 & 0.55 & 0.53 & 100\% & 0.49 & 0.54 & 0.55 & 0.53 \\ \hline \multicolumn{1}{c}{(e) random sampling} & & & & & & & & & \\ \hline  & SVM-BOW & SVM-FT & RF-BOW & RF-FT & & & & & \\ \hline
5\% & 0.19 & 0.29 & 0.33 & 0.33 & & & & & \\ \(10\%\) & 0.25 & 0.37 & 0.38 & 0.39 & & & & & \\ \(25\%\) & 0.37 & 0.45 & 0.46 & 0.45 & & & & & \\ \(33\%\) & 0.40 & 0.47 & 0.47 & 0.47 & & & & & \\ \(50\%\) & 0.44 & 0.50 & 0.50 & 0.49 & & & & & \\ \(66\%\) & 0.47 & 0.52 & 0.52 & 0.51 & & & & & \\ \(75\%\) & 0.47 & 0.52 & 0.53 & 0.52 & & & & & \\ \(100\%\) & 0.49 & 0.54 & 0.55 & 0.53 & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average PR AUC values for cluster initialization. * Diversity sampling performs worse than random sampling, yielding a lower level of prediction quality at nearly all stages of active learning. To verify how the predictive performance achieved during active learning relates to dataset properties, Table 5 presents the Spearman rank correlation values of the area under the precision-recall curve at the active learning stage of 33% labeled data to dataset size and relevant class percentage. Rows correspond to active learning initialization methods and query selection strategies, and columns correspond to classification algorithms and text representation methods. It can be immediately seen that in all cases, the predictive performance is strongly correlated negatively to data size and positively to relevant class percentage. While the latter is not at all surprising, with less imbalance leading to better classification, the former may appear to contradict the expectation of more data, leading to better classification. However, this expectation would be justified only if considering the increase of the training data size for the same classification task. Here, different datasets correspond to different classification tasks, and bigger datasets are likely to contain more articles that are hard to correctly separate. While all the examined algorithm configurations tend to work better with smaller and less imbalanced datasets than with bigger and more imbalanced ones, it is interesting whether these data properties also impact the relative performance of different active learning query selection strategies.