# UFODMV: Unsupervised Feature Selection for Online Dynamic Multi-Views
## 6 Results and Discussion
### Classification Accuracy
Additionally, when UFODMV is compared with the online multi-view method (OMVFS), it has significantly better recall, precision, and f-measure for all the used datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method**Number of Selected Features & **25** & **50** & **75** & **100** & **Metric** \\ \hline UFODMV (proposed) & **0.926** & **0.936** & **0.976** & **0.979** & \\ \hline OMVFS & 0.116 & 0.118 & 0.127 & 0.147 & Recall \\ \hline SPEC & 0.855 & 0.864 & 0.909 & 0.895 & \\ \hline UFSSF & 0.857 & 0.884 & 0.916 & 0.938 & \\ \hline UFODMV (proposed) & **0.929** & **0.939** & **0.976** & **0.979** & \\ \hline OMVFS & 0.119 & 0.123 & 0.133 & 0.152 & Precision \\ \hline SPEC & 0.857 & 0.856 & 0.909 & 0.896 & \\ \hline UFSSF & 0.858 & 0.874 & 0.916 & 0.938 & \\ \hline UFODMV (proposed) & **0.926** & **0.939** & **0.976** & **0.979** & \\ \hline OMVFS & 0.116 & 0.117 & 0.128 & 0.148 & F-measure \\ \hline SPEC & 0.853 & 0.854 & 0.909 & 0.895 & \\ \hline UFSSF & 0.857 & 0.863 & 0.912 & 0.937 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification accuracy using Handwritten dataset with IB1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method**Number of Selected Features & **25** & **50** & **75** & **100** & **Metric** \\ \hline UFODMV (proposed) & **0.861** & **0.886** & **0.965** & **0.961** & \\ \hline OMVFS & 0.288 & 0.299 & 0.334 & 0.349 & Recall \\ \hline SPEC & 0.804 & 0.866 & 0.891 & 0.907 & \\ \hline UFSSF & 0.81 & 0.867 & 0.912 & 0.922 & \\ \hline UFODMV (proposed) & **0.862** & **0.887** & **0.956** & **0.972** & \\ \hline OMVFS & 0.284 & 0.329 & 0.342 & 0.349 & Precision \\ \hline SPEC & 0.804 & 0.869 & 0.892 & 0.908 & \\ \hline UFSSF & 0.823 & 0.869 & 0.914 & 0.945 & \\ \hline UFODMV (proposed) & **0.861** & **0.886** & **0.955** & **0.961** & \\ \hline OMVFS & 0.269 & 0.270 & 0.307 & 0.342 & \\ \hline SPEC & 0.803 & 0.867 & 0.891 & 0.906 & \\ \hline UFSSF & 0.821 & 0.866 & 0.91 & 0.923 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification accuracy using Handwritten dataset with naive Bayes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method’Number of Selected Features** & **25** & **50** & **75** & **100** & **Metric** \\ \hline UFODMV (proposed) & **0.891** & **0.927** & **0.935** & **0.939** & \\ \hline OMVFS & 0.371 & 0.379 & 0.382 & 0.387 & Recall \\ \hline SPEC & 0.739 & 0.77 & 0.778 & 0.78 & Recall \\ \hline UFSSF & 0.879 & 0.881 & 0.912 & 0.919 & \\ \hline UFODMV (proposed) & **0.88** & **0.926** & **0.931** & **0.937** & \\ \hline OMVFS & 0.374 & 0.377 & 0.392 & 0.393 & Precision \\ \hline SPEC & 0.736 & 0.791 & 0.815 & 0.819 & \\ \hline UFSSF & 0.872 & 0.873 & 0.906 & 0.912 & \\ \hline UFODMV (proposed) & **0.883** & **0.923** & **0.931** & **0.935** & \\ \hline OMVFS & 0.373 & 0.377 & 0.386 & 0.389 & \\ \hline SPEC & 0.734 & 0.771 & 0.781 & 0.782 & \\ \hline UFSSF & 0.868 & 0.869 & 0.906 & 0.912 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification accuracy using Caltech dataset with IB1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method’Number of Selected Features** & **25** & **50** & **75** & **100** & **Metric** \\ \hline UFODMV (proposed) & **0.561** & **0.687** & **0.746** & **0.764** & \\ \hline OMVFS & 0.354 & 0.169 & 0.149 & 0.157 & Recall \\ \hline SPEC & 0.379 & 0.399 & 0.42 & 0.442 & \\ \hline UFSSF & 0.389 & 0.433 & 0.51 & 0.539 & \\ \hline UFODMV (proposed) & **0.641** & **0.721** & **0.763** & **0.777** & \\ \hline OMVFS & 0.304 & 0.254 & 0.359 & 0.324 & Precision \\ \hline SPEC & 0.428 & 0.462 & 0.473 & 0.514 & \\ \hline UFSSF & 0.518 & 0.598 & 0.6278 & 0.624 & \\ \hline UFODMV (proposed) & **0.562** & **0.693** & **0.75** & **0.768** & \\ \hline OMVFS & 0.316 & 0.163 & 0.122 & 0.123 & \\ \hline SPEC & 0.469 & 0.514 & & \\ \hline UFSSF & 0.517 & 0.592 & 0.618 & 0.613 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification accuracy using Fox News dataset with naive Bayes.