Optimizing Weighted Extreme Learning Machines for imbalanced classification and application to credit card fraud detection

The classification problems with imbalanced datasets widely exist in real word. An Extreme Learning Machine is found unsuitable for imbalanced classification problems. This work applies a Weighted Extreme Learning Machine (WELM) to handle them. Its two parameters are found to affect its performance greatly. The aim of this work is to apply various intelligent optimization methods to optimize a WELM and compare their performance in imbalanced classification. Experimental results show that WELM with a dandelion algorithm with probability-based mutation can perform better than WELM with improved particle swarm optimization, bat algorithm, genetic algorithm, dandelion algorithm and self-learning dandelion algorithm. In addition, the proposed algorithm is applied to credit card fraud detection. The results show that it can achieve high detection performance.