Modeling of Failure Prediction Bayesian Network with Divide-and-Conquer Principle

Table 3

Fitness values and corresponding iterations of each algorithm for every dataset.

Runs	Dataset 1		Dataset 2		Dataset 3
	Iteration	Fitness	Iteration	Fitness	Iteration	Fitness
	FPBN-DC
1	14	3.828	12	2.475	8	1.636
2	14	3.828	7	2.475	5	1.636
3	11	3.828	6	2.475	93	1.636
4	9	3.828	15	2.475	6	1.636
5	8	3.828	7	2.475	15	1.636
6	8	3.828	16	2.475	6	1.636
7	17	3.828	15	2.475	10	1.636
8	10	3.828	9	2.475	9	1.636
9	12	3.828	12	2.475	15	1.636
10	13	3.828	6	2.475	8	1.636
Average	11.6	3.828	10.5	2.475	17.5	1.636
	BN-IA
1	79	3.828	99	2.475	79	1.636
2	38	3.828	58	2.475	93	1.636
3	80	3.79	42	2.473	64	1.636
4	92	3.828	30	2.475	99	1.635
5	56	3.827	59	2.47	70	1.634
6	95	3.824	85	2.474	82	1.626
7	97	3.828	58	2.475	75	1.636
8	86	3.828	83	2.47	89	1.634
9	43	3.828	88	2.472	47	1.634
10	60	3.828	69	2.472	56	1.636
Average	72.6	3.8237	67.1	2.4731	75.4	1.6343