Towards augmented kernel extreme learning models for bankruptcy prediction: Algorithmic behavior and comprehensive analysis

Abstract

Bankruptcy prediction is a crucial application in financial fields to aid in accurate decision making for business enterprises. Many models may stagnate to low-accuracy results due to the uninformed choice of parameters. This paper presents a forward-thinking bankruptcy prediction model based on kernel extreme learning machine (KELM), which proposes a new efficient version of a fruit fly optimization (FOA) algorithm called LSEOFOA, to evolve and harmonize the penalty and the kernel parameter in KELM. The upgraded version of FOA is conceptualized based on three reorganizations. The first attempt is to include Levy's flight for improving exploration inclinations, and the second is based on slime mould algorithm (SMA) for avoiding premature convergence and enhancing the stability of the exploration and exploitation patterns. As the last modification, we utilized the elite opposition-based learning for accelerating the convergence. The algorithmic trends of this optimizer are verified, and then, it is verified on a bankruptcy prediction module. Therefore, to further demonstrate the superiority of the LSEOFOA method, comparison studies are performed using the conventional FOA and other variants of FOA and a set of advanced algorithms including EBOwithCMAR. Experimental results for every optimization task demonstrate that LSEOFOA can provide a high-performance and self-assured tradeoff between exploration and exploitation. Also, the developed KELM classifier is utilized for bankruptcy prediction, and its optimal parameters set are revealed by the proposed FOA. The effectiveness of the LSEOFOA-KELM model is rigorously evaluated using a financial dataset and comparison with KELM-based models with other competitive optimizers such as LSHADE-RSP. Overall research findings show that the proposed model is superior in terms of classification accuracy, Matthews correlation coefficient, sensitivity, and specificity. Towards more evolutionary and efficient prediction models, the proposed LSEOFOA-KELM prediction model can be regarded as a promising warning tool for financial decision making, with successful performance in bankruptcy prediction. Interested readers to the idea and related material of LSEOFOA-KELM can find the designed public web service at https://aliasgharheidari.com. Also, the info and source codes of the slime mould algorithm (SMA) in python, matlab and other languages are shared publicly at https://aliasgharheidari.com/SMA.html.