## 4 Experimental Setup and Results
### Svm  
For the analysis of the attributes in the SVM classifier, we perform several experiments to analyze the role of reducing the attributes and see whether it decreases or increases performance. For this, we use the many useful approaches available in the state-of-the-art. These are:  
* Subset Evaluation proposed [45]: Evaluates the importance of a reduced set of attributes taking into account the individual-based predictive capability of every attribute and measuring the redundancy between them. * Correlation Evaluation [46]: Considers the importance of feature by analyzing the correlation between the feature and the class variable. * Gain Ratio Evaluation [47]: Considers the importance of a feature by analyzing the gain ratio with respect to the class. * Info Gain Evaluation [47]: Considers the importance of an attribute that measures the information gain to the corresponding class. * OneR Evaluation [48]: Uses the OneR classifier for the attribute role in model building. * Principal Components [49]: Evaluates transformation and the principal components analysis of the data. * Relief Evaluation [50]: Finds the importance of an attribute by repeated sampling and considers the value of the attribute for the nearest instance of the similar and different class. * Symmetrical Uncertain Evaluation [51]: Considers the attribute importance by measuring the symmetrical uncertainty against the class variable. All these approaches provide a rich and complete set of feature selection methods and are representative of the complete framework for similar tasks. Table 1 shows the different approaches and the attributes selected by them. Actual features mean the attributes that are returned by feature  
Figure 1: Sample images from dataset [21]. extraction methods and not the feature selection method. The other eight starting from the Subset Evaluation are the selection methods and select the appropriate number of attributes depending on the algorithm. The Subset Evaluation selects 84 important features and discards others. The Correlation Evaluation, Gain Ration Evaluation, Info Gain Evaluation, OneR Evaluation, Relief Evaluation, and the Symmetrical Uncertain Evaluation ranks features according to the importance. The 200 most important features (returned by the importance) are selected from these algorithms. 200 attributes are enough as the Subset Evaluation selects only 84 attributes out of 1024 attributes. The Principal Components returns 258 important components from the data. The result of the attributes selected by these algorithms are interesting, Figures 2 and 3 show the corresponding feature selection algorithm and the F-measure using the classifier SVM and the Random Forest respectively. In Figure 2, SVM is used for model learning and validation. In Figure 3, the Random Forest is used for model generation and corresponding evaluation. In Figure 2, for SVM, the F-measure of different data attributes does not have the same behavior. Although the number of selected attributes in all algorithms is less than the number of attributes of the actual data, two algorithms have higher F-measure than the F-measure of the actual data attributes. Even though the other F-measure values are less than that of actual data, the considerable reduction in the dataset by attribute selection methods should not be neglected. The F-measure of the actual data attributes which are 1024 is 0.784. The F-measure for Subset Evaluation is 0.76, Correlation Evaluation is 0.786, the Gain Ration Evaluation is 0.783, the Info Gain Evaluation is 0.77, One \begin{table}
\begin{tabular}{c c} \hline \hline
**Approach** & **Attributes** \\ \hline Actual Attributes Before Selection & 1024 \\ Subset Evaluation & 84 \\ Correlation Evaluation & 200 \\ Gain Ratio Evaluation & 200 \\ Info Gain Evaluation & 200 \\ OneR Evaluation & 200 \\ Principal Components & 258 \\ Relief Evaluation & 200 \\ \hline \hline Symmetrical Uncert. Evaluation & 200 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Total number of attributes selected by the different algorithms.