Analysis of Bayesian optimization algorithms for big data classification based on Map Reduce framework

Banchhor, Chitrakant; Srinivasu, N.

doi:10.1186/s40537-021-00464-4

Journal of Big Data

Table 2 Analysis of CNB and CGCNB

From: Analysis of Bayesian optimization algorithms for big data classification based on Map Reduce framework

Training data (%)	Mappers (M)	Accuracy (%)	Sensitivity (%)	Specificity (%)	Memory (MB)	Time (s)
NB
75	2	68	69.4	61	39.8	38.4
	3	68.1	69.6	61.1	39	36.5
	4	68.2	69.8	61.3	38.2	32
	5	68.3	70.2	61.5	37	30.5
80	2	69.5	70.5	62.2	39.5	29.6
	3	69.7	70.7	62.3	38.2	29.2
	4	69.9	71.2	62.4	37.7	28.7
	5	70.1	71.4	62.5	36.8	28.4
85	2	71.6	71.6	62.6	33.3	27.8
	3	71.7	71.9	62.8	33.1	27
	4	71.8	72	62.9	32.6	26.2
	5	72.1	72.1	63.1	32.4	25.5
90	2	74.6	72.6	65.2	34.5	24.4
	3	74.8	72.8	65.7	33.1	23.8
	4	75	73.1	65.9	31.8	22.1
	5	75.2	73.3	66.1	30.1	21.2
CNB
75	2	73.6	75.8	64.7	39.7	31.8
	3	73.7	75.9	64.8	38.8	30.8
	4	73.9	76.1	65	37.1	29.9
	5	74.1	76.2	65.2	36.2	29
80	2	74.8	77.1	65.3	36.8	29.9
	3	74.9	77.2	65.4	36.4	29.4
	4	75	77.4	65.5	36	29
	5	75.1	77.5	65.6	35.8	28.8
85	2	76.2	78.2	65.7	35.5	28.6
	3	76.3	78.3	65.8	34.4	27.9
	4	76.4	78.4	66	33.5	27.1
	5	76.6	78.5	66.1	32.2	26.6
90	2	77.5	79.6	68.2	32.1	26.8
	3	77.7	79.7	68.5	31.4	25.9
	4	77.8	79.8	68.7	30.9	25
	5	77.9	79.9	68.8	30.5	24.1
GWO + CNB
75	2	74	76.6	66.6	37.4	29.4
	3	74.1	76.8	66.8	36.6	28.9
	4	74.2	76.9	67	36	28.6
	5	74.3	77.1	67.1	35.8	28.2
80	2	74.7	77.7	66.8	36.3	29.6
	3	74.9	77.9	66.9	35.7	28.4
	4	75	78.2	67.1	35.1	27.9
	5	75.1	78.5	67.2	34.4	27.7
85	2	75.7	77.8	67.9	35.5	28.8
	3	75.8	77.9	68.1	34.1	28.1
	4	75.9	78	68.2	33.2	27.4
	5	76.1	78.1	68.4	32.3	26.5
90	2	76.9	79.6	69.5	33.8	27.7
	3	77.1	79.7	69.6	33.7	26.5
	4	77.2	79.8	69.8	32.4	25.9
	5	77.4	79.9	69.9	31.2	25.5
CGCNB
75	2	75.1	81.1	64.6	36.4	28.8
	3	75.2	81.3	64.8	35.8	28.2
	4	75.4	81.4	65	34.1	26.5
	5	75.5	81.5	65.1	32.7	25.5
80	2	75.7	81.5	64.8	34.4	27.2
	3	75.8	81.6	64.9	33.6	26.4
	4	76.1	81.7	65.1	32.1	25.4
	5	76.2	81.9	65.2	31.2	24.1
85	2	76.7	81.9	66.2	34.5	24.4
	3	76.8	82.1	66.4	33.2	23.8
	4	76.9	82.3	66.5	31.2	23
	5	77.1	82.4	66.7	30.6	22.2
90	2	78.7	82.1	66.8	31.2	23.8
	3	78.9	82.2	66.9	30.7	22.1
	4	79	82.3	67	30.7	21.7
	5	79.1	82.5	67.1	29.8	20.5
FCNB
75	2	77.7	79.8	70.7	36.6	24.4
	3	77.8	79.9	70.9	35.4	23.4
	4	78	80.1	71.1	32.4	22.9
	5	78.2	80.2	71.2	31.7	22
80	2	78.5	80.5	71.8	33.3	24.3
	3	78.6	80.7	71.9	32.1	23.7
	4	78.7	80.9	72.1	31.7	22.3
	5	78.8	81.1	72.3	30.6	21.2
85	2	78.8	81.5	73.1	31.2	22.2
	3	78.9	81.7	73.3	30.5	21.3
	4	79	82	73.4	29.9	20.6
	5	79.1	82.1	73.5	29.6	19.8
90	2	79.6	82.7	73.6	27.8	19.9
	3	79.8	82.9	73.7	26.6	18.4
	4	79.9	83.2	73.8	26	17.1
	5	80.1	83.4	73.9	25.5	16.8
HCNB
75	2	78.8	80.9	71.8	31.2	14.5
	3	78.9	81	71.9	30.7	13.8
	4	79.1	81.2	72	30	11.2
	5	79.2	81.4	72.2	29.5	10.5
80	2	79.7	81.7	72.5	31.4	13.3
	3	79.9	81.9	72.6	30.9	12.1
	4	80.1	82.1	72.7	29.8	11.5
	5	80.2	82.2	72.8	29.1	10.3
85	2	80.9	83	73.8	28.6	9.92
	3	81.2	83.2	73.9	27.9	9.83
	4	81.3	83.4	74	27.1	9.79
	5	81.4	83.5	74.1	26.8	9.76
90	2	81.8	83.7	74.6	26.7	9.55
	3	82	83.9	74.7	25.9	9.42
	4	82.1	84	74.9	25	9.26
	5	82.2	84.1	75.1	24.1	9.12

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