An Integration of Archerfish Hunter Spotted Hyena Optimization and Improved ELM Classifier for Multicollinear Big Data Classification Tasks

摘要

Big data mining has emerged as an active field of interest, and traditional data mining approaches frequently fail to handle the complexities associated with massive datasets. One of the most extensively used strategies for big data classification is MapReduce, which combines the map and reduce processes. For filtering and sorting, the mapping approach is employed, and the reduction technique is used to combine the final classification results. A novel Archerfish Hunter Spotted Hyena Optimization-based Improved Extreme Learning Machine (AHSHO-IELM) classifier-based MapReduce framework is proposed in this paper for big data classification. The IELM algorithm is formed by integrating the ELM technique with Principal Component Analysis to overcome the multicollinear problem and enhance the training and testing time. The AHSHO method combines the archerfish hunter optimization and Spotted Hyena Optimization algorithms to improve the optimal parameter selection of the IELM classifier, which increases classification accuracy and reduces the error rate. The performance of the proposed AHSHO-IELM classifier-based MapReduce framework is evaluated using different performance metrics such as Accuracy, Sensitivity, Specificity, Computational time, F1-Score, Mathews correlation coefficient, and scale-up factor. For the rotten tomatoes movie review dataset and the Dermatology dataset, the proposed approach yields accuracy, specificity, and sensitivity values of 99%, 99%, 98.3%, and 99.3%, 99%, 98%, respectively for a mapper value (X) of 5. The proposed big data classifier is effective for both single-class and multi-class classification, according to the results of the analysis.