A computational pipeline for the development of multi-marker bio-signature panels and ensemble classifiers

BMC Bioinformatics

Table 2 Summary of classification performance for the Average Probability aggregation method

AVERAGE PROBABILITY	Sensitivity				Specificity				AUC
	Ensemble classifier	Individual classifiers			Ensemble classifier	Individual classifiers			Ensemble classifier	Individual classifiers
	Ensemble classifier	min	max	average	Ensemble classifier	min	max	average	Ensemble classifier	min	max	average
Ensemble 1	0.73	0.64	0.73	0.68	0.90	0.90	0.95	0.93	0.95	0.73	0.94	0.84
Ensemble 2	0.82	0.55	0.82	0.69	0.95	0.86	1.00	0.93	0.98	0.73	0.96	0.88
Ensemble 3	0.73	0.64	0.73	0.65	0.95	0.81	0.95	0.91	0.97	0.73	0.94	0.88
Ensemble 4	0.82	0.55	0.73	0.64	0.90	0.81	1.00	0.92	0.97	0.83	0.95	0.90
Ensemble 5	0.82	0.55	0.82	0.66	0.95	0.81	1.00	0.92	0.98	0.73	0.96	0.89

Shown is classification performance as measured by sensitivity, specificity and AUC – for the 5 ensembles defined in Table 1 when using the average probability aggregation method. The minimum, maximum and average performances of individual classifiers in the respective ensemble are included in the table for comparison.

ISSN: 1471-2105