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

BMC Bioinformatics

Table 6 Summary of classification performance for the Vote Threshold aggregation method

VOTE THRESHOLD	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 A	1.0000	0.9211	0.9737	0.9474	0.7368	0.8421	0.8421	0.8421	0.9875	0.9626	0.9737	0.9681
Ensemble B	1.0000	0.9211	0.9211	0.9211	0.6842	0.8421	0.8421	0.8421	0.9917	0.9557	0.9612	0.9584
Ensemble C	1.0000	0.9211	0.9737	0.9539	0.6842	0.7368	0.8947	0.8421	0.9861	0.9501	0.9709	0.9602
Ensemble D	1.0000	0.9211	0.9737	0.9386	0.7368	0.8421	0.9474	0.9035	0.9875	0.9557	0.9778	0.9677
Ensemble E	1.0000	0.8947	1.0000	0.9518	0.6316	0.7368	0.9474	0.8553	0.9903	0.9418	0.9765	0.9643
Ensemble F	1.0000	0.9211	0.9737	0.9430	0.6842	0.8421	0.9474	0.8816	0.9931	0.9557	0.9848	0.9672

Shown is performance for tumour vs normal classification for the 6 ensembles defined in Table 4 using the vote threshold aggregation method. Similarly to Table 5, individual classifier performances are included for comparison.

ISSN: 1471-2105