# UFODMV: Unsupervised Feature Selection for Online Dynamic Multi-Views
## 5 Experimental Evaluation  
This section describes the experimental setup of the proposed UFODMV and the benchmark methods for real datasets. The experiments were conducted to investigate the following:  
* How the selected set of representative features affects the classification accuracy on online dynamic multi-view data for various feature sets;
* The speed of UFODMV when selecting a set of representative features in the aforementioned environment. To simulate the online learning and the dynamic views, the datasets were structured as shown in Figure 4, where every new chunk has new instances and a view. The number of chunks was set to be equal to the number of the views, and the chunk size was the number of data instances divided by the number of views. The chunks were structured this way as it matched the settings of real applications where both views and instances can increase over time. In a hospital, new patients might have already undergone various medical tests. Additionally, existing patients may undergo a new set of medical tests. To properly answer the two questions above, UFODMV was benchmarked against three well-known unsupervised FS methods, namely OMVFS [17], UFSSF [34], and SPEC [4]. To the best of our knowledge, to date, there are no unsupervised FS methods that have been developed for dynamic-view applications. Therefore, these three methods have been selected as they comprise a combination of streaming features, batch, online, and multiple view applications. A summary of these methods is provided in Table 2. SPEC and UFSSF cannot be applied directly to dynamic views, as SPEC is designed only for batched datasets, and UFSSF is designed for streaming features. Therefore, the views of each dataset were concatenated in a single matrix. Then, these two methods were applied to that data matrix. For OMVFS, we followed the settings given in [17], and we set the chunk size of each view to be equal to the chunk size used in UFODMV in order to make a fair comparison. An exception is the dynamic views, as OMVFS works with multiple but not dynamic views. \(\alpha_{v}\) and \(\beta_{v}\) remained the same for all different views. We conducted a grid search in \(\{10^{-2},10^{-1},10^{1},10^{2}\}\) and selected the one with better results. \(\gamma\) was set to \(10^{7}\). We used the code ([[https://github.com/software-shoo/Online-Unsupervised-Multiview-Feature-Selection/blob/master/OMVFS.m](https://github.com/software-shoo/Online-Unsupervised-Multiview-Feature-Selection/blob/master/OMVFS.m)]([https://github.com/software-shoo/Online-Unsupervised-Multiview-Feature-Selection/blob/master/OMVFS.m](https://github.com/software-shoo/Online-Unsupervised-Multiview-Feature-Selection/blob/master/OMVFS.m))) (accessed on 20 January 2023) as directed in [17]. For UFODMV, we reported the selected features of the last chunk in order to make a fair comparison with the benchmark methods. The radius was set such that we could obtain \(k\) number of clusters or selected features. \(k\) was set to the required number of representative features to be selected. For all the evaluated methods, the number of selected features was varied to ensure the reliability of the results. The representative features selected by UFODMV and the other benchmark methods were evaluated using two well-known classifiers: naive Bayes [46] and lazy nearest neighbor [47] (also called IB1). In addition to the classifiers, \(k\)-fold cross-validation was applied to all selected features to avoid the problem of overfitting the data and to produce accurate results. First, the set of selected features was divided into subsets of equal size depending on the selected \(k\) folds. One subset was retained as the testing subset, and the remainder were used as training subsets. Finally, the average value of all