Table 7.

Comparisons between previous works and the proposed method.

Authors	Noiseless data	Imperfect data	Classifiers	Accuracy
Sharma et al., 2014 [19]	Two different classes	N/A	LS-SVM	85%
Sadati et al., 2006 [15]	A, D, and E	N/A	SVM, FBNN, ANFIS, and proposed ANFN	85.9%
Mohamed et al., 2013 [13]	A, B, C, D, and E	N/A	NB, MLP, k-NN, LDA, and SVM	89.5%
Liang et al., 2010 [44]	A, D, and E	N/A	PCA and GA	80%–90%
Tzallas et al., 2009 [11]	A, B, C, D, and E	N/A	ANN	89%
Proposed method	A, C, and E	A, C, and E	Ensemble NSC	90%