# Online Streaming Feature Selection via Conditional Independence
## 5 Experiments and Analysis
### Comparison of ConInd with Two Online Algorithms
#### 5.3.3 Variation in the Number of Features and Running Time with the Increase of Feature Ratio
For the datasets of ionosphere and marti1, the number of relevant features was only 25 and 1, respectively. Therefore, the running time for _OSFS_ was lower than that for _ConInd_. This is because the filtering for single-conditional independence is not necessary for a few non-redundant features. For the first four datasets in Table 8, _OSFS_ significantly outperformed _ConInd_ over many datasets in terms of the number of selected features and running time. By contrast, it was observed that, to the right of the data in Table 8, _ConInd_ significantly outperformed _OSFS_. The best result for the _ConInd_ algorithm is highlighted in bold on the right side of Table 8. The representative datasets include sylva, cina0, lucap0, and reged1. The results indicate that the _ConInd_ algorithm achieved better results than _OSFS_ in candidate feature sets with low redundancy and high relevance. _OFFSET_ is suitable for high redundancy feature streaming and spare candidate feature sets. The encouraging results verify the efficacy of the _ConInd_ algorithm for datasets with low redundancy and high relevance. As illustrated in Figure 8, _ConInd_ outperformed _OSFS_ on the cina0, sylva, lucap0, and reged1 datasets. As the size of the features increased, the running time for _OSFS_ increased rapidly, while the running time for _ConInd_ remained stable. It can also be seen in Table 8 that the four datasets have very few redundant features. Meanwhile, there is higher number of _SF_ in the two algorithms. The number of _SF_ was more than 10, e.g., for _OSFS_, 18 for sylva, 22 for cina0, 36 for ucap0, and 13 for reged1. For _ConInd_, the number change from #_SIC_ to _SF_ was \(52\to 24\), \(57\to 30\), \(49\to 40\), and \(16\to 13\) for the sylva, cina0, lucap0, and reged1 datasets, respectively. As illustrated in Figure 9, _OSFS_ outperformed _ConInd_ on the ionosphere, leukemia, marti1, and arcene datasets. The running time for _OSFS_ remained stable as the size of the features increased, while the running time for _ConInd_ increased rapidly. It can also be seen in Table 8 that the four datasets have very few features in _SF_ or many redundant features. Meanwhile, the num  
Figure 8: The variation in running time with increase in ratio feature number in datasets with low redundancy and high relevance. algorithms was less than 10; e.g., for \(OSFS\), there are four for ionosphere, three for leukemia, one for marti1, and five for arcene. For \(ConInd\), the number variation from \(\#SIC\) to \(SF\) was \(7\to 5\), \(47\to 8\), \(1\to 1\), and \(13\to 6\) for the ionosphere, leukemia, marti1, and arcene datasets, respectively. To sum up, Figure 8 shows that \(ConInd\) significantly improves performance in the aspect for running time while the datasets is lowly redundant and highly relevant. This is because the number of selected features is quite large in these datasets. This means that, compared with \(ConInd\), \(OSFS\) will spend more time dealing with redundant features. In contrast, Figure 9 shows that, with the increase of feature size, \(ConInd\) is not suitable for processing the datasets with high redundancy and low relevance. The reason is that the filtering of single-conditional independence would spend some time. Instead, when the number of selected features is very small, \(OSFS\) uses very little time to process redundant features. Therefore, from the perspective of time performance, \(ConInd\) is more suitable for dealing with the datasets with low redundancy and high relevance.