Evolutionary Spiking Neural Networks for Solving Supervised Classification Problems

Table 3

Accuracy of design and testing on every configuration for Balance Scale, Blood, Breast Cancer, Card, Diabetes, and Fertility datasets.

Dataset	Configuration	Design accuracy	Test accuracy
Balance Scale	α1	0.7486 ± 0.0525	0.7219 ± 0.0629
	α2	0.8354 ± 0.0460	0.8077 ± 0.0582
	β1	0.7331 ± 0.0718	0.6944 ± 0.0752
	β2	0.8363 ± 0.0272	0.8078 ± 0.0427
	γ1	0.8528 ± 0.0197	0.8346 ± 0.0261
	γ2	0.8960 ± 0.0062	0.8647 ± 0.0134
Blood	α1	0.7711 ± 0.0112	0.7622 ± 0.0117
	α2	0.8010 ± 0.0135	0.7731 ± 0.0168
	β1	0.7747 ± 0.0110	0.7607 ± 0.0138
	β2	0.7863 ± 0.0162	0.7684 ± 0.0120
	γ1	0.7760 ± 0.0076	0.7618 ± 0.0088
	γ2	0.7957 ± 0.0145	0.7685 ± 0.0155
Breast Cancer	α1	0.9494 ± 0.0141	0.9418 ± 0.0238
	α2	0.9781 ± 0.0073	0.9585 ± 0.0077
	β1	0.9474 ± 0.0121	0.9405 ± 0.0151
	β2	0.9677 ± 0.0073	0.9432 ± 0.0142
	γ1	0.9574 ± 0.0111	0.9384 ± 0.0140
	γ2	0.9749 ± 0.0062	0.9478 ± 0.0117
Card	α1	0.8624 ± 0.0099	0.8641 ± 0.0140
	α2	0.8779 ± 0.0160	0.8591 ± 0.0174
	β1	0.8561 ± 0.0502	0.8524 ± 0.0527
	β2	0.8814 ± 0.0137	0.8561 ± 0.0153
	γ1	0.8740 ± 0.0134	0.8596 ± 0.0197
	γ2	0.8879 ± 0.0120	0.8535 ± 0.0166
Diabetes	α1	0.7506 ± 0.0231	0.7457 ± 0.0207
	α2	0.7843 ± 0.0156	0.7476 ± 0.0224
	β1	0.7570 ± 0.0151	0.7490 ± 0.0215
	β2	0.7780 ± 0.0143	0.7477 ± 0.0126
	γ1	0.7810 ± 0.0153	0.7370 ± 0.0152
	γ2	0.7902 ± 0.0134	0.7389 ± 0.0205
Fertility	α1	0.8988 ± 0.0256	0.8218 ± 0.0616
	α2	0.9297 ± 0.0204	0.8170 ± 0.0551
	β1	0.9255 ± 0.0243	0.8309 ± 0.0459
	β2	0.9182 ± 0.0222	0.8279 ± 0.0467
	γ1	0.9455 ± 0.0199	0.8479 ± 0.0462
	γ2	0.9370 ± 0.0131	0.8236 ± 0.0484