Knowledge Extraction About Beer Classification Using Evolving Fuzzy Neural Networks

Evolving Fuzzy Neural Networks (EFNNs) are well-regarded for their interpretability and proficiency in pattern classification tasks. However, their accuracy may need to be improved when confronted with limited samples for specific classes or the emergence of new classes in the data stream. To overcome this limitation, we applied the EFNN-Gen, a novel approach that integrates a priori knowledge through generalist rules to solve a beer classification problem. These rules are derived from assessing the specificity of Gaussian functions within the first layer neurons of the EFNN. They represent expert knowledge about the classification problem and are aimed at enhancing the network's performance. Experimental tests conducted on the Beer dataset, a real-world multiclass pattern classification dataset, demonstrate that integrating generalist rules leads to a significant accuracy improvement of 97.14%.