Training Spiking Neural Models Using Artificial Bee Colony

Table 3

Average accuracy provided by the FNN using different databases.

	FNN		FNN		FNN		FNN
Dataset	DG-1		DG-2		LM-1		LM-2
	Tr. cr.	Te. cr.	Tr. cr.	Te. cr.	Tr. cr.	Te. cr.	Tr. cr.	Te. cr.
Wine	0.9867	0.9800	0.9497	0.9171	0.9986	0.9714	0.9797	0.9629
Iris plant	0.9133	0.8867	0.6625	0.6367	0.9942	0.9767	0.7667	0.7733
Glass	0.6907	0.6738	0.6390	0.6310	0.8453	0.7714	0.7413	0.7119
Diabetes	0.7663	0.7732	0.7153	0.7144	0.7993	0.7320	0.7850	0.7588
Liver	0.5924	0.6145	0.5928	0.5623	0.7243	0.6652	0.7181	0.6812
Object recognition	0.7413	0.6800	0.7413	0.6800	0.7413	0.6800	0.7413	0.6800

Tr. cr = training classification rate, Te. cr. = testing classification rate.