A Novel Approach to Model Ensembled-Based ANFIS for Big Data

This study aims to develop a new ANFIS-based ensemble modeling approach that provides high prediction accuracy and generalization capability on large datasets. The proposed approach utilizes the parallel processing capacity of the MapReduce algorithm to divide large datasets into smaller chunks and create and train independent ANFIS models for each chunk. While the input and output membership functions obtained from the trained structures are directly transferred to the new architecture, the rule bases are integrated using the rule adjustment function. The number of rules has been significantly reduced compared to the classical ANFIS structure. In this way, both the computational cost has been reduced and the model complexity has been effectively managed. In traditional ensemble approaches found in the literature, the output values of the models are generally combined, whereas in this study, the proposed approach combines the ANFIS structures obtained from each subset of the data to create a single ANFIS-based ensemble model. The obtained results demonstrate that a single ensemble system architecture, encompassing the entire large dataset and possessing high generalization capability, has been successfully created.