A Novel Fuzzy Kernel Extreme Learning Machine Algorithm in Classification Problems

Today, numerous methods have been developed to address various problems, each with its own advantages and limitations. To overcome these limitations, hybrid structures that integrate multiple techniques have emerged as effective computational methods, offering superior performance and efficiency compared to single-method solutions. In this paper, we introduce a basic method that combines the strengths of fuzzy logic, wavelet theory, and kernel-based extreme learning machines to efficiently classify facial expressions. We call this method the Fuzzy Wavelet Mexican Hat Kernel Extreme Learning Machine. To evaluate the classification performance of this mathematically defined hybrid method, we apply it to both an original dataset and the JAFFE dataset. The method is enhanced with various feature extraction methods. On the JAFFE dataset, the algorithm achieved an average classification accuracy of 94.55% when supported with local binary patterns and 94.27% with a histogram of oriented gradients. Moreover, these results outperform those of previous studies conducted on the same dataset. On the original dataset, the proposed method was compared with an extreme learning machine and wavelet neural network, and it was found that the method has remarkable efficiency compared to the other two methods.