A Differential Evolution-Oriented Pruning Neural Network Model for Bankruptcy Prediction

Table 12

Classification accuracies for Qualitative Bankruptcy dataset obtained by other hybrid classification methods in relative literatures.

Author (year)	Method (train-to-test ratios)	Average accuracy (%)
Yi Tan et. al. (2016) [44]	Hybrid logistic regression-naive bayes (90%-10%))	99.64
Nanxi Wang (2017) [45]	Neural network model with robust logistic regression (50%-50%)	69.44
Nanxi Wang (2017) [45]	Neural network model with inductive learning algorithm (50%-50%)	89.7
Nanxi Wang (2017) [45]	Neural network model with genetic algorithm (50%-50%)	94
Nanxi Wang (2017) [45]	Neural network model with neural networks without dropout (50%-50%)	90.3
Nanxi Wang (2017) [45]	Neural network model with SVM (50%-50%)	98.67
Nanxi Wang (2017) [45]	Neural network model with decision tree (50%-50%)	99.33
J.Uthayakumar et. al. (2018) [43]	Genetic ant colony algorithm (Not mentioned)	91.32
J.Uthayakumar et. al. (2018) [43]	Fitness-scaling chaotic Genetic ant colony algorithm (Not mentioned)	92.14
J.Uthayakumar et. al. (2018) [43]	Improved K-means clustering and fitness-scaling	97.93
	chaotic genetic ant colony algorithm (Not mentioned)
Our Method (2019)	EPNN (50%-50%)	99.57
Our Method (2019)	EPNN (10 fold cross validation)	99.68