## 7 Results
### Experiment 3: Dataset Dimension  
Some questions arise at this point about the database dimensionality. How does this affect the performance? Is the classifier able to learn more with a higher number of samples? Is the classifier able to get the same performance with a lower number of data, that is without the use of augmented data? Thus, we tested with several subsets of 20 diatom types and 40 diatom types with 300 samples per diatom; a subset with the 35 classes that had a minimum of 100 samples per diatom; as well as with 80 diatom types with 2000 samples per diatom versus the previous dataset with 300 samples per diatom. To extend the dataset, a data augmentation with rotations every 2\({}^{\circ}\) was performed. Figure 9 shows the results when classifying with the bagging DT and 10 fcv. It represents the minimum and maximum _Acc._ value obtained in the different trials. The decrease in the number of classes with 300 samples per class increased its accuracy, though not significantly. Additionally, the increase in the number of samples lowered the error. The use of 100 samples per class classifying 35 classes with no data augmentation decreased accuracy to 96.25% versus 98.11% with 300 samples per class. A similar decrease happens when classifying 20 classes, where an accuracy of 97.56% (100 samples/class without data augmentation) is obtained versus 98.82% (300 samples/class with data augmentation). Results are usually illustrated with confusion matrices. Each column of a confusion matrix represents the instances in a predicted class while each row represents the instances in an actual class. Since there are many classes in this study, heat maps have been used to display the % of correct and incorrect classifications. A heat map displays the confusion matrix as an image whose color intensities reflect the magnitude of its values. In this case, green values indicate the % of correct classifications and pink-red values indicate the % of instances incorrectly classified. These percentages, as well as the true positive rate (green column) and false negative rate (pink-red column) for each class are shown when no more than 20 classes are classified, as well as the true positive rate (green column) and false negative rate (pink-red column). The confusion matrix heat map for the classification of the 80 diatom classes with the bagging tree is shown in Figure 10. Figure 11 shows the confusion matrix heat map for the classification of the diatom classes without data augmentation. Figure 11 shows 20 classes chosen randomly for visualization purpose. The confusion matrix heat map of several trials done with the 20 classes is shown in Figure 12. The main diagonal of Figure 12 shows how some classes obtained 100% correct classification. Figure 13 shows those diatoms species that produce the major misclassification errors, i.e., _Nitzschia costei_, _Gomphonema angustatum_, _Gomphonema micropumum_, _Gomphonema minusculum_ and _Gomphonema minutum_. Automation methods may be a tool to help both the expert and the non-expert, but in difficult cases, it should always be the expert who makes the final decision. We believe that it is the diatomist who must consider if an automatic classification system is acceptable, assuming a certain  
Figure 8: Accuracy performance combining descriptor types and using 300 samples per class. level of accuracy to classify different taxa. A reject option is also possible, so that only difficult cases are presented to the diatomist, while the easy ones are classified automatically. Notice that this methodology may be applied to other taxa not included in this study. Moreover, it is currently being applied to another 20 different taxa not shown in this study, and similar results are being obtained. Figure 10: Confusion matrix classifying 80 classes with the bagging tree classifier 10-fold cross-validation (10 fcv). Figure 9: Accuracy performance with different numbers of classes using the bagging DT classifier. No data augmentation is applied when using 100 samples per class.