# Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species
## 4 Results
### Effectiveness of the Proposed RNN-CM Model  
To evaluate the effectiveness of our RNN-CM model, we compared the accuracy of classification based on the proposed representative segments among different algorithms, including two traditional classification methods, namely, Linear Support Vector Machine (Linear SVM) [30; 31] and Random Forest methods [32]. These two traditional algorithms have been widely used for data classification [33]. We first trained each algorithm and then used it to identify representative segments with confidence scores from the same given dataset. In Linear SVM, the confidence score of a datum is the signed distance of that sample to the hyperplane. In Random Forest, there are multiple decision trees and the confidence score is the mean predicted class probability of the trees in the forest. Then, we compared their accuracy, which is measured as the ratio of correct classifications to all classifications.  
The results in Table 1 reveal that none of the algorithms perform well when the whole dataset is taken into account, regardless of the confidence scores (the \"All\" column). This is because seals in different groups can have lots of similar segments as they belong to the same species, and thus, it is difficult for a classification algorithm to identify the labels of these similar segments in high accuracy. In this case, classifiers perform as random estimators and the accuracy is around 50% for the two class classification.  
If we raise the confidence threshold as shown from right to left, i.e., if we consider only the predictions with the top \(X\%\)\((X=10,20,30)\) confidence scores by each algorithm, the accuracy is increasing because classifiers gradually ignore low confidence estimations and concentrate on high confidence ones. In such a case, the accuracy of our approach is significantly higher than that of the other approaches. This is because our approach with deep learning architecture can better describe latent patterns in the data than other approaches, so that the prediction based on such patterns can be more accurate. In addition, our confidence measure is integrated together with the classifier during training, so that it can be better optimized than training the classifier only. We also present the male  
Figure 3: Histograms of distances and angles for trajectory segments with different confidences. Representative segments are those with high confidences, while common segments are those with low confidences. \(t\) = 0,1,2,3,4,5 are time units of one segment. (**a**) Representative segments; (**b**) Common segments.  
-seal segment fraction of the top \(X\%\) (\(X=10,20,30\)) confidence segments, as indicated in brackets of Table 1. These fractions indicate that most of the representative segments belong to males.  
In short, if some trajectory patterns are shared by different animal groups, a classification algorithm can be confused and cannot make correct group predictions, and consequently gives low confidence scores for such patterns. On the other hand, trajectory segments that can earn high confidence scores are generally patterns that are unique to the corresponding animal group. Thus, our algorithm can identify group-specific patterns more accurately.  
In the experiment, the training segments and the testing segments are from different seals. The consistency of the testing results and the training objectives also indicates that animals in the same group can share some patterns of trajectory segments.