# A Novel Fuzzy Kernel Extreme Learning Machine Algorithm in Classification Problems
## 3 Experimental Results and Analysis
The confusion matrix obtained for this experiment is presented in Table 2.  
Table 2 shows that the performance of the classifier is 97.78% for happiness, 98.89% for fear and 84.22% for disgust. When the algorithm was run with histograms of oriented gradients, it was found that the algorithm correctly classified over 90% for all expressions except disgust. The average classification accuracy was calculated as 94.27%. The developed \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **H** & **S** & **A** & **F** & **D** & **Sur.** & **N** \\ \hline Happy (H) & **96.13** & 1.29 & 0 & 0 & 0.65 & 0 & 1.93 \\ Sad (S) & 2.58 & **94.96** & 0 & 0.55 & 0.62 & 0 & 1.29 \\ Anger (A) & 0 & 0 & **94** & 0.65 & 2.67 & 0 & 2.67 \\ Fear (F) & 0 & 2.5 & 0.63 & **91.89** & 1.88 & 3.1 & 0 \\ Disgust (D) & 0 & 0.69 & 0 & 2.76 & **96.55** & 0 & 0 \\ Surprise (Sur.) & 0 & 0.37 & 0 & 0.67 & 0 & **97.63** & 1.33 \\ Neural (N) & 2.76 & 5.27 & 0.62 & 0.67 & 0 & 0 & **90.67** \\ \hline \end{tabular}
\end{table} 
Table 1: Classification results: the confusion matrix of the LBP + FWMHKELM approach. \begin{table}
\begin{tabular}{l c c c c c c c} \hline  & **H** & **S** & **A** & **F** & **D** & **Sur.** & **N** \\ \hline H & **97.78** & 1.11 & 0 & 0 & 0 & 0 & 1.11 \\ S & 0 & **95.65** & 3.24 & 0 & 0 & 1.11 & 0 \\ A & 0 & 0 & **97.78** & 1.11 & 1.11 & 0 & 0 \\ F & 0 & 1.11 & 0 & **98.89** & 0 & 0 & 0 \\ D & 0 & 0 & 0 & 0 & **84.22** & 15.78 & 0 \\ Sur. & 2.22 & 0 & 0 & 4.44 & 0 & **90.01** & 3.33 \\ N & 3.33 & 0 & 1.11 & 0 & 0 & 0 & **95.56** \\ \hline \end{tabular}
\end{table} 
Table 2: Classification results: the confusion matrix of the HOG + FWMHKELM approach. algorithm is also compared with other existing studies in the literature using the same dataset and the results are presented in Table 3.  
Table 3 shows that the hybrid algorithm and the local binary pattern algorithm work together in feature detection to achieve more efficient results. In the second experiment, original images were used to test our approach. The dataset consists of images of 38 people of Turkish origin, 17 males and 21 females, each showing six basic facial expressions (happy, sad, fearful, angry, surprised, and neutral). In the first step of the process, Ethics Committee Approval was obtained for the creation of the dataset and the necessary personal data sharing consents were obtained from the participants through the Voluntary Participation Form. To balance gender and age distribution and reduce sampling bias, the study protocol was designed to include stratified participant recruitment. Neutral images were captured twice under controlled lighting conditions to minimize within-dataset bias. The original image size is 768 \(\times\) 1024 pixels. Image samples from the database are shown in Figure 8.  
The pseudocode of this experiment is presented in Figure 9. The hybrid method was again run on MATLAB R2021b software. The data were split 70:30 into training data and test data. This ratio was chosen because the neural network was intended to be trained with maximum data, and the remaining images were sufficient to test the accuracy of the algorithm. In the preprocessing step, the image intensities were normalized and only the image consisting of a face expressing an emotion was preprocessed and resized. On the other hand, gray processing was used on the face images to remove the effects of lighting. Histogram equalization was applied for gray processing. Using the Viola-Jones algorithm, certain features in the images were cropped and the face region of the image was extracted. Log Gabor filters were used for feature extraction. Figure 8: Sample images from the original database (approved by the Ethics Committee). \begin{table}
\begin{tabular}{l c} \hline \hline
**Methods** & **Average Recognition Rates (\%)** \\ \hline LBP + FWMHKELM & 94.55 \\ HOG + FWMHKELM & 94.27 \\ Fisherface + BPNN [28] & 86.85 \\ MIP + ELM [9] & 90.16 \\ HOG + ELM [29] & 94.37 \\ PCA + FLD + WNN [30] & 90.00 \\ LM + FWNN [31] & 92.00 \\ \hline \hline \end{tabular}
\end{table}