# UFODMV: Unsupervised Feature Selection for Online Dynamic Multi-Views
## 6 Results and Discussion
### Classification Accuracy  
The classification accuracy was measured based on three evaluation metrics: recall, precision, and F-measure. Two classifiers were used, namely naive Bayes and IB1. Then, we present the results relating to the running time. The time taken by each benchmark method to select features is reported in seconds. The experimental results regarding the classification accuracy of UFODMV and the benchmark methods on the three datasets are presented in Tables 3-8. The following observations can be made from the results:  
* With all methods, the accuracy (recall, precision, and f-measure) increases as the number of the selected features increases, thus indicating that all methods selected good representative features. * UFODMV significantly outperformed the traditional/single view feature selection methods as it incrementally exploited the correlation between the views. Therefore, it selected better representative features. * UFODMV was also compared with a multi-view feature selection method, namely OMVFS. However, UFODMV consistently has the highest recall, precision, and f-measure with all different numbers of selected features. This is because UFODMV incrementally updates the means of the clusters to evaluate the representativeness of the selected features, as these might lose their representativeness over time due to the arrival of new features. * For example, Tables 3 and 4 show the classification accuracy of UFODMV and the benchmark methods on the Handwritten dataset. For both the naive Bayes and IB1 classifiers, UFODMV has around 5-7% better accuracy compared to the traditional/single-view methods. Additionally, it has around 50% better accuracy compared to OMVFS regardless of whether the classification metric is recall, precision, or f-measure. Table 7 shows the classification accuracy of UFODMV when applied to the Fox News dataset using the naive Bayes classifier. UFODMV has around 15-20% better accuracy when compared with traditional/single-view methods. Additionally, it achieves around 40-60% better accuracy than OMVFS. * The method with the second-best accuracy is UFSSF. This is valid for almost all datasets with all evaluation classifiers. Indeed, although UFSSF was not developed for multi-view data, it incrementally clusters the features in a streaming feature manner. * For the Fox News dataset, all the methods have an overall reduction in accuracy when they are evaluated using the IB1 classifier. This might be due to the Fox News data being sparse, since the IB1 classifier is efficient when classifying dense data. UFODMV has better accuracy compared with the benchmark methods. This is due to the fact that UFODMV has an efficient clustering methodology. In fact, when partitioning the clusters, UFODMV allows the features to be clustered naturally based on their similarities, and later on, it merges the clusters into the required clusters. This clustering methodology ensures that features are well partitioned or grouped, and good clustering will definitely result in a good representation of the features. Additionally, UFODMV limited the representative feature of each cluster as the one with minimum distance to its cluster's centroid. This step ensures that the feature selected from each cluster is a good representative of other features in that cluster. Finally, because views are dynamic, the set of features were updated at each clustering step, as some features might be representative for a limited amount of time. Although in a few cases, UFSSF achieved better accuracy than the proposed UFODMV, the latter addressed the problem of increasing instances (i.e., online learning) and increasing views (dynamic views). UFSSF only addressed the dynamic features problem (i.e., increasing features), and therefore, we can accept the tiny accuracy difference in a few cases while addressing the problem of both increasing features and increasing instances.