A Pruning Neural Network Model in Credit Classification Analysis

Table 8

Classification accuracy rates comparison between PNN and other algorithms obtained from literatures of the Australian credit dataset.

Authors (published year)	Algorithms (train-to-test ratios)	Classification accuracy rate (%)
Luo et al. (2009) [48]	SVM (10 CV)	80.43
Peng et al. (2011) [49]	Bayesian network (10 CV)	85.22
	KNN (10 CV)	79.42
	RBF network (10 CV)	83.04
Yu et al. (2011) [1]	C4.5 (10 CV)	84.3
	LVQ (10 CV)	82.97
Chang and Yeh (2012) [50]	SVM (10 CV)	84.7
	C4.5 (10 CV)	82.5
	Naive Bayes (10 CV)	84.9
Zhu et al. (2013) [51]	QDA (5 CV)	80.02
	DT (5 CV)	83.18
Tsai et al. (2014) [52]	MLP (10 CV)	82.44
	DT (10 CV)	84.91
Lessmann et al. (2015) [8]	CART (10 CV)	66.4
	ELM (10 CV)	69.8
	LDA (10 CV)	78.9
	Logistic regression (10 CV)	80.7
	ADT (10 CV)	79.8
	Bag (10 CV)	76.8
	Boost (10 CV)	81
	Random forest (10 CV)	85.2
Khashei and Mirahmadi (2015) [7]	QDA (50%-50%)	80.1
	SVM (50%-50%)	77.5
Our method (2017)	PNN (50%-50%)	85.64
	PNN (5 CV)	85.31
	PNN (10 CV)	85.19