Table 5.

Performance analysis of CNS dataset using rbio2.2 with variations in window sizes and levels.

Classifier	Window size	Classification accuracy (%)
		Level 1	Level 2	Level 3	Level 4	Level 5	Level 6	Level 7
KNN	64	57.46	55.41	60.67	0.00	0.00	0.00	0.00
	128	58.11	54.68	53.29	58.55	0.00	0.00	0.00
	256	55.41	63.74	47.73	59.94	58.92	0.00	0.00
	512	64.11	54.39	48.76	60.96	70.03	62.72	0.00
	1024	59.28	55.77	59.58	48.76	62.72	54.68	50.88
Bayes	64	50.15	63.01	67.91	0.00	0.00	0.00	0.00
	128	60.23	57.82	69.30	67.62	0.00	0.00	0.00
	256	54.31	51.54	49.12	70.76	56.43	0.00	0.00
	512	59.21	59.94	53.29	53.29	59.21	59.94	0.00
	1024	54.02	48.46	62.72	50.88	58.55	54.68	55.77
SVM	64	62.72	63.74	65.20	0.00	0.00	0.00	0.00
	128	73.54	64.11	57.82	59.94	0.00	0.00	0.00
	256	59.58	52.63	47.00	64.47	51.61	0.00	0.00
	512	51.24	48.76	57.82	45.98	60.60	49.49	0.00
	1024	49.49	57.09	67.25	48.39	69.37	47.73	39.69
Hybrid	64	100.00	90.00	95.00	0.00	0.00	0.00	0.00
	128	100.00	96.67	86.67	75.00	0.00	0.00	0.00
	256	96.67	96.67	95.00	90.00	90.00	0.00	0.00
	512	93.33	78.33	81.67	98.33	100.00	98.33	0.00
	1024	90.00	70.00	86.67	73.33	73.33	86.67	70.00