Grey wolf optimization evolving kernel extreme learning machine: Application to bankruptcy prediction. (August 2017) - Wang, Mingjing; Chen, Huiling; Li, Huaizhong; Cai, Zhennao; Zhao, Xuehua; Tong, Changfei; Li, Jun; Xu, Xin
Abstract: This study proposes a new kernel extreme learning machine (KELM) parameter tuning strategy using a novel swarm intelligence algorithm called grey wolf optimization (GWO). GWO, which simulates the social hierarchy and hunting behavior of grey wolves in nature, is adopted to construct an effective KELM model for bankruptcy prediction. The derived model GWO-KELM is rigorously compared with three competitive KELM methods, which are typical in a comprehensive set of methods including particle swarm optimization-based KELM, genetic algorithm-based KELM, grid-search technique-based KELM, extreme learning machine, improved extreme learning machine, support vector machines and random forest, on two real-life datasets via 10-fold cross validation analysis. Results obtained clearly confirm the superiority of the developed model in terms of classification accuracy (training, validation, test), Type I error, Type II error, area under the receiver operating characteristic curve (AUC) criterion as well as computational time. Therefore, the proposed GWO-KELM prediction model is promising to serve as a powerful early warning tool with excellent performance for bankruptcy prediction. Graphical abstract: Highlights: A new KELM parameter optimization strategy based on grey wolf optimization (GWO) is presented for bankruptcy prediction. The effectiveness of the proposed method has been rigorously estimated on the real life data by comparing with the widely used machine learning methods. The proposed method has achieved not only very promising classification performance but also with less computational time.