@misc{10481/76619,
year = {2022},
month = {7},
url = {http://hdl.handle.net/10481/76619},
abstract = {Financial distress prediction is crucial in the financial domain because of its implications
for banks, businesses, and corporations. Serious financial losses may occur because of poor financial
distress prediction. As a result, significant efforts have been made to develop prediction models
that can assist decision-makers to anticipate events before they occur and avoid bankruptcy, thereby
helping to improve the quality of such tasks. Because of the usual highly imbalanced distribution
of data, financial distress prediction is a challenging task. Hence, a wide range of methods and
algorithms have been developed over recent decades to address the classification of imbalanced
datasets. Metaheuristic optimization-based artificial neural networks have shown exciting results in a
variety of applications, as well as classification problems. However, less consideration has been paid to
using a cost sensitivity fitness function in metaheuristic optimization-based artificial neural networks
to solve the financial distress prediction problem. In this work, we propose ENS_PSONNcost and
ENS_CSONNcost: metaheuristic optimization-based artificial neural networks that utilize a particle
swarm optimizer and a competitive swarm optimizer and five cost sensitivity fitness functions as
the base learners in a majority voting ensemble learning paradigm. Three extremely imbalanced
datasets from Spanish, Taiwanese, and Polish companies were considered to avoid dataset bias.
The results showed significant improvements in the g-mean (the geometric mean of sensitivity and
specificity) metric and the F1 score (the harmonic mean of precision and sensitivity) while maintaining
adequately high accuracy.},
organization = {Spanish Government	PID2020-115570GB-C22},
publisher = {MDPI},
keywords = {Financial distress},
keywords = {Cost-sensitive},
keywords = {Ensemble learning},
keywords = {Imbalanced classification},
keywords = {Metaheuristic},
keywords = {Neural networks},
title = {Cost-Sensitive Metaheuristic Optimization-Based Neural Network with Ensemble Learning for Financial Distress Prediction},
doi = {10.3390/app12146918},
author = {Al-Deen Safi, Salah and Castillo Valdivieso, Pedro Ángel and Faris, Hossam},
}