# Multi-Input Convolutional Neural Networks for Automatic Pollen Classification
## 3 Results and Discussions
### Classification after Hyper-Parameter Tuning  
In this section, we evaluate the results of the best model obtained in the hyperparameter search and compare it with the results from previous works on all four datasets.  
In Table 6, we can see that all models gained accuracy using the best hyperparameter configuration found. The gains were not equal across the board with most of the improvement having been obtained by the models trained on SAU-SRB and MARS. These were the most difficult datasets when looking at the number of classes. The increase in performance is more visible when looking at each individual feature extractor. Comparing rows 5-7 of Table 6 with rows 4-6 from Table 5, we can see that all the feature extractors were much better at making classifications. Examining the error rate for the scattering image feature across all four datasets, we obtained a drop from an average of 42% in the initial case to 38% using the best architecture found. For the fluorescence spectrum, the improvement was event greater, from an average error rate value of 46% to 37%. Finally, for the lifetime signal, the average error rate across the datasets decreased from 44% to 40%. In an ideal scenario, these improvements should translate to an similar improvement in the combined model. However, what we observed in practice is that the combined model error rate decreased but at diminishing returns. With the fine tuned architecture, compared to the initially proposed architecture, we obtained a relative reduction in the error rate of 13%, from 23% to 20%, for SAU-SRB; of 12%, from 16% to 14%, for SAU-LI; of 13%, from 15% to 13%, for SAU-CH; and of 20%, from 24% to 19%, for MARS.  
The diminishing returns observed in the model accuracy are explained by the fact that each feature extractor worked well for a sub-set of the whole dataset and the combined model worked well on the union of these sub-sets, that were well classified by each feature extractor. When the accuracy of a feature extractor rises this means that the sub-set of samples that are correctly classified increases. When examining all three feature extractors globally, we did not obtain the same increase in performance, because there was an increase in the overlap of samples that were classified by each feature extractor, as can be seen in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data-Set** & **SAU-SRB** & **SAU-LI** & **SAU-CH** & **MARS** \\ \hline Baseline 1  & 74\% & 73\% & 80\% & - \\ \hline Initial architecture 2  & 77\% & 84\% & 85\% & 76\% \\ \hline Best architecture & 80\% & 86\% & 87\% & 81\% \\ \hline Scattering image only & 58\% & 62\% & 61\% & 64\% \\ \hline Fluorescence spectrum only & 56\% & 72\% & 66\% & 58\% \\ \hline Lifetime signal only & 68\% & 59\% & 72\% & 41\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model performance after hyperparameter tuning (accuracy).  
From the point of view of the combined model, the improvement seen by each feature extractor translates to a higher confidence, rather than a higher overall accuracy.  
Considering at the results of the hyperparameter experiments, we can see that part of the error in classification is inherent to each of the datasets and difficult to overcome without an in depth analysis of the source of such errors. In the following subsection, the classification error is described using the confusion matrix.