## 4 Experimental Setup and Results
### Random Forest  
Figure 3 shows the analysis done with the Random Forest, and has almost similar trends as Figure 2. However, in Figure 3, for the Random Forest with actual attributes of 1024, the F-measure is 0.841, and no higher F-measure has been recorded for any of the used algorithms. The F-measure for Subset Evaluation is 0.83, Correlation Evaluation is 0.824, the Gain Ration Evaluation is 0.782, the Info Gain Evaluation is 0.825, OneR Evaluation is 0.833, Principal Components is 0.806, Relief Evaluation is 0.838, and the Symmetrical Uncertain Evaluation is 0.812. Contrary to the SVM case, the Correlation Evaluation, and the Relief Evaluation F-measure is slightly lower than the actual F-measure of 0.841. The lowest F-measure of 0.782 is obtained for Gain Ration Evaluation. The Subset Evaluation has slightly less F-measure (0.83) than the actual attributes (0.841). Despite none of the F-measure values is higher than F-measure of the actual attributes, the performance can be viewed as progressing, keeping in mind the great reduction in the dataset compared to the actual data.  
In general, in Figure 3, the decreasing trend in the F-measure is low compared to the number of attributes decreased. As 40 GB (100%) data is represented with the 1024 attributes, one of the  
Figure 3: Random Forest performance based on the F-measure for the Actual 1024 attributes and the F-measure based on the attributes selected by the corresponding algorithm.  
beauties of the attribute selection methods is providing a reduced set of data. Instead of processing the actual set of data attributes, a reasonable performance can be achieved with a great reduction in data attributes, and consequently a great improvement in data processing and time. Let us analyze the attribute selection method. For this, we select Relief Evaluation from Figure 3. The Relief Evaluation uses 200 attributes and achieves an F-measure of 0.838. This means that while just using the 19% data from the actual data of 1024 attributes and over 40 GB, we get only a 1% decrease in classification and recognition performance. From data perspectives, only 19 GB data, from the 100 GB data will approximately give the same performance of the 100 GB data set. The OneR Evaluation has also similar results with only a 1% decrease in performance. Without a doubt, the slight decrease in performance still can be considered positively as long as a great reduction in the data processing. The lowest F-measure is Gain Ratio Evaluation, which has almost a 6% decreased performance. Even, with 6%, we get an 81% decrease in data processing and time. As the point of this article is to analyze the impact of reducing data on performance, these results show an interesting trend towards processing big data with acceptable results. One other interesting insight noted in Figures 2 and 3 is that Principal Components has comparatively reduced performance. One reason is that it may not be thoroughly investigated with the other algorithms as is done in this experimentation setup. Finally, the work in this paper presents a continuation of sampling strategies of the previous work in [46; 47] and thus augments the related domain with new experiments and results.