# Machine Learning Powered Microalgae Classification by Use of Polarized Light Scattering Data
## 3 Results
### Classification Result of the Dataset  
Since SVM is designed for two-class tasks, the dataset was firstly separated by OVR or OVO into two categories. For OVR, every target class was compared with the remaining classes, and 35 classifiers could be built with the non-linear SVM with OVR. For an unknown validating sample, 35 classifiers were applied to predict the measured data and output 35 probability scores, then the highest score corresponded to the identified category.  
Different levels of recognition accuracy were set in order to quantitatively analyze the classification performance with the built classifier. Moreover, it could provide some information about the microalgae, which is important for understanding the physical mechanism of the discrimination or instructing the future prototype deployments in water. Empirically, we selected four levels to evaluate the results. The performance of the classifier on the validating dataset is presented in Table 3, which shows that _Isochrysis_ has the highest recognition accuracy greater than 0.95, which implies that _Isochrysis_ has distinct scattering property compared with the other microalgae in Table 1. Referring to the result in Table 3, both _Chattonella marina_ and _Cryptomonas_ achieved a recognition accuracy greater than 0.9 but smaller than 0.95, and _Chaetoceros curviseus_ and _Zoxxanthella_ achieved an accuracy falling in the range of 0.85-0.90. In addition, the other five categories could be identified with an accuracy within 0.80-0.85. Based on the OVR approach, there were in total 10 categories of microalgae that could be identified in the dataset with an accuracy greater than 0.80. The classifiers derived from OVR were able to recognize the certain targeted category of microalgae, and the trained model could be used to evaluate whether the measured sample was similar to the targeted category.  
For OVO, every class was respectively compared with the remaining \(n-1\) classes, and then \(n\times(n-1)/2\) classifiers could be built in total. During the validating process, the identified category was voted among these classifiers. Different from OVR, we set an accuracy threshold to find the largest subset out of the original dataset, and all of the microalgae in the subset could be recognized above the accuracy threshold. Subsequently, different accuracy thresholds were compared, including 0.95, 0.90, 0.85, and 0.80. With these thresholds, the results of the largest subset are shown in Table 4.  
The results in Table 4 show that four categories (\(n=4\)) of microalgae could be extracted from the original dataset, and each category in the subset could be retrieved with an accuracy of more than 0.95. Eight categories of microalgae could be extracted out of the dataset, and they could be recognized with an accuracy above 0.9, and 11 and 15 categories of microalgae could be extracted if the accuracy threshold was 0.85 and 0.8, respectively.  
Comparing these two approaches, the OVO performed better than the OVR. However, it is notable that the complexity of OVO is O(\(n^{2}\)) which is much higher than that of OVR. Different from OVR, the OVO for SVM in this work tried to find the subset of the microalgae where different categories of microalgae could be well classified from others. Then, these categories of microalgae in the subset could be used as the skeleton categories which are independent of each other and are the basis of the dataset, and the other categories are similar to them or their combinations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Recognition Accuracy** & **Categories of Microalgae** \\ \hline \(>\)0.95 & _Isochrysis_ \\
0.90–0.95 & _Chattonella marina_; _Cryptomonas_ \\
0.85–0.90 & _Chaetoceros curviseus_; _Zoxxanthella_ \\
0.80–0.85 & _Porphylaceae_; _Trichodesminum_; _Amphidinium carterae_ \\ & _Hulburt_; _Amphora_; _Skeletonema costatum_ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification results using SVM by one-versus-rest (OVR).