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 1 Analysis of NB, CNB, GWO + CNB, CGCNB, FCNB, and HCNB based on localization data

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

Training data (%)	Mappers (M)	Accuracy (%)	Sensitivity (%)	Specificity (%)	Memory (MB)	Execution time (s)
NB
75	2	76.4	80.4	72.4	39.7	2.87
	3	76.1	80.5	72	38.4	2.98
	4	76.1	80.2	72.2	37.5	2.91
	5	76.3	80.1	72.1	36.3	2.75
80	2	76.7	80.6	72.5	38.8	2.75
	3	76.5	80.5	72.1	37.3	2.85
	4	76.2	80.5	72.5	36.6	2.65
	5	76.5	80.3	72.1	36.6	2.68
85	2	76.8	80.7	72.7	36.5	2.64
	3	76.7	80.6	72.5	36.9	2.73
	4	76.2	80.7	72.5	34.7	2.41
	5	76.9	80.4	72.7	34.1	2.49
90	2	76.9	80.9	72.9	34.6	2.42
	3	76.8	80.8	72.8	35.5	2.58
	4	76.5	80.7	72.9	32.5	2.35
	5	76.9	80.9	72.7	31.1	2.37
CNB
75	2	77.1	81	73	39.7	2.27
	3	77	81.3	73.2	35.6	2.32
	4	77.5	81.2	73.4	40.2	2.55
	5	77.1	81	73.4	37.6	2.47
80	2	77.5	81.3	73.2	39.4	2.12
	3	77.3	81.5	73.6	34.1	2.21
	4	77.6	81.5	73.6	38.5	2.32
	5	77.3	81	73.6	36.7	2.56
85	2	77.7	81.4	73.5	38.8	2.01
	3	77.4	81.7	73.8	33.5	2.16
	4	77.9	81.7	73.7	37.4	2.16
	5	77.6	81.4	73.7	35.5	2.26
90	2	77.8	81.7	73.9	35.8	1.99
	3	77.9	81.9	73.8	32.9	2.08
	4	77.9	81.9	73.9	35.3	1.95
	5	77.8	81.5	73.7	34.8	2.22
GWO + CNB
75	2	79.1	82.5	75.1	62.7	2.73
	3	79.4	82.9	75.3	61.2	2.64
	4	79.2	82.9	75.2	60.6	2.88
	5	79.2	82.9	75.1	59.8	2.61
80	2	79.2	82.7	75.3	61.2	2.66
	3	79.5	82.9	75.5	59.6	2.56
	4	79.5	83	75.3	59.2	2.33
	5	79.5	82.9	75.1	58.8	2.49
85	2	79.4	82.9	75.4	59.6	2.48
	3	79.6	83	75.7	58.4	2.36
	4	79.7	83.2	75.7	57.9	2.21
	5	79.6	83	75.2	57.3	2.16
90	2	79.9	82.9	75.7	58.1	2.32
	3	79.7	83.3	75.7	57.7	2.11
	4	79.9	83.2	75.9	54.5	2.09
	5	79.8	83.4	75.9	55.9	2.02
CGCNB
75	2	80	83.7	76.2	12.9	2.98
	3	80	83.5	76.3	13.3	2.87
	4	80.5	83.9	76.5	14.7	2.55
	5	80.1	83.6	76	13.7	2.63
80	2	80.4	83.9	76.3	12.2	2.81
	3	80.3	83.6	76.3	12.7	2.82
	4	80.5	84	76.6	13.9	2.45
	5	80.2	83.9	76.2	13	2.36
85	2	80.8	84	76.5	11.4	2.73
	3	80.3	84	76.7	11.8	2.73
	4	80.7	84.2	76.9	12.4	2.26
	5	80.4	84	76.7	12.3	2.27
90	2	80.9	84	76.6	10.2	2.58
	3	80.4	84.1	76.7	10.5	2.53
	4	80.9	84.4	76.9	10.9	2.17
	5	80.7	84.5	76.9	11.1	2.09
FCNB
75	2	93.1	96.2	91.5	823	3.33
	3	93.4	96.3	91	812	3.45
	4	93.3	96.5	91.2	826	3.19
	5	93.5	96.4	91.3	815	3.06
80	2	93.2	96.1	91.3	836	3.08
	3	93.6	96.4	91.4	831	3.02
	4	93.4	96.6	91.2	819	2.98
	5	93.3	96.3	91.5	813	2.91
85	2	93.2	96.1	91.2	845	2.55
	3	93.2	96.4	91.3	833	2.41
	4	93.4	96.3	91.5	824	2.22
	5	93.5	96.7	91.5	809	2.05
90	2	93.1	96.7	91.7	812	1.53
	3	93.7	96.6	91.9	826	1.23
	4	93.8	97	91.7	814	1.11
	5	93.9	97.1	91.8	803	1.05
HCNB
75	2	85	94	89.2	166	1.99
	3	85.1	94.3	89.3	158	1.90
	4	85.2	94.2	89.1	137	1.88
	5	85.3	94.2	89.3	122	1.81
80	2	85.4	94.1	89.4	133	1.87
	3	85.3	94.2	89	122	1.77
	4	85.3	94.3	89.3	119	1.71
	5	85.4	94.5	89.6	118	1.67
85	2	85.3	94.3	89.7	122	1.77
	3	85.5	94.6	89.5	118	1.63
	4	85.4	94.7	89.4	115	1.57
	5	85.6	94.9	89.6	113	1.52
90	2	85.6	95	89.8	136	1.36
	3	85.7	95.2	89.5	131	1.44
	4	85.7	94.7	89.6	113	1.24
	5	85.9	94.8	90	105	1.11

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