## 4 Results and Discussion
### Ensemble Evaluation  
The fusion of base classifiers reveals in most cases (60/64) an improvement of classification performance on the test data compared to the best base classifier of each ensemble (see Table 5).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**FEATURE** & \multicolumn{3}{c}{**TEXT**} & \multicolumn{3}{c}{**TEXT**} & \multicolumn{3}{c}{**TEXT**} \\  & **BPRbottom** & & **BPRtop** & **BPRbottom** & & **BPRtop** \\ \hline Base Classifier & LogReg & & SVM & LogReg & & SVM \\ F1 & 0.606 & & 0.691 & 0.606 & & 0.691 \\ Fusion method & & **Stacking** & & & **Fixed Combination** & \\ \hline
**IPC** & & & **Fusion** & & & **Fusion** & \\
**BPRbottom** & & 0.691 & SVM & 0.702 & 0.681 & sum & 0.702 \\ kNN & 0.543 & 0.670 & kNN & 0.681 & 0.660 & product & 0.713 \\  & 0.660 & LogReg & 0.702 & 0.649 & min & 0.681 \\  & & 0.691 & ANN & 0.745 & 0.681 & max & 0.702 \\
**BPRtop** & & 0.713 & SVM & 0.755 & 0.734 & sum & 0.745 \\ LogReg & 0.681 & 0.745 & kNN & 0.745 & 0.734 & product & 0.745 \\  & & 0.755 & LogReg & 0.755 & 0.702 & min & 0.713 \\  & & 0.766 & **ANN** & **0.787** & 0.723 & max & 0.723 \\
**CPC** & & & & & & \\
**BPRbottom** & & 0.638 & SVM & 0.723 & 0.681 & sum & 0.702 \\  & & 0.691 & kNN & 0.713 & 0.660 & product & 0.681 \\ kNN & 0.532 & 0.670 & LogReg & 0.745 & 0.660 & min & 0.681 \\  & & 0.670 & ANN & 0.745 & 0.638 & max & 0.691 \\
**BPRtop** & & 0.681 & SVM & 0.766 & 0.766 & **sum** & **0.777** \\  & & 0.766 & kNN & 0.745 & 0.734 & **product** & **0.777** \\ ANN & 0.681 & 0.745 & LogReg & 0.777 & 0.713 & min & 0.745 \\  & & 0.702 & **ANN** & **0.787** & 0.734 & max & 0.755 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Resulting performance (F1) of the ensemble classification displayed as pivot table of different base classifiers combinations using stacking or fixed combinations rules as fusion method. The combination of the base learners in pairs of one text and one taxonomic (either IPC or CPC) classifier is additionally varied according to the Base Performance Ranking (BPR: top or bottom rank). For each of those conditions four different stacking classifiers as well four fixed-combinations rules are applied (see Table 4). Peak values are printed in bold.  
Figure 4: Performance of four different base classifiers using text or taxonomic (IPC, CPC) features comparing the overall F1 scores. Biomedical patents of the test data set were classified. IPC: International Patent Classification.  
The improvements reach a maximum of 9.6%, which in two cases leads to the peak value of the entire performance matrix of F1 = 78.7%, namely by combining SVM (Text) with ANN (IPC) or ANN (CPC). At this stage, two first conclusions can be drawn: (1) in our approach, ensemble classification using text and taxonomic features in general proves to be a very effective method to enhance the overall classification performance; (2) our achieved maximum value certainly holds up to the comparison to multi-classifier fusion with F1 = 77.6% [13] before adding active learning components or network-based classification with F1 = 76.2% [7]. The latter approach only reaches F1 = 86.4% by hybridization of four different patent classification approaches. The impressive performance is counterbalanced by a high complexity in using a wide variety of methods and distinct data sources. In contrast, for our model the IPC/CPC codes and titles/abstracts of the patents are readily available from the same structured database [32]. Except DSE, the deployed feature transformations, the base classifiers, and fusion methods are all well-established, which further strengthen the applicability and efficiency of the presented pipeline. Finally, the fusion by the fixed combination rules sum and product achieved remarkable top scores of F1 = 77.7%, only one mark behind the overall peak using stacking, without the burden of training and hyperparameter tuning.