Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes

Table 5

Voting results of OV.

Platforms	Class	Vote from all methods	Vote after filtering	MCC avg	MCC max
OV-mRNA	bayes	0.4725	0.4725	0.1217	0.7906
	functions	0.1250	0.4725	−0.0890	0.7906
	lazy	0.3162	0.3162	0.1328	0.6447
	meta	0.1890	0.1890	0.1923	0.6447
	misc	0.6139	0.7500	0.3433	0.8864
	rules	0.1250	0.3162	−0.1160	0.8864
	trees	0.1890	0.1890	0.1734	0.6447
	overall	0.3162	0.3162	0.0890	0.8864
OV-miRNA	bayes	1.0000	0.8771	0.2508	0.8864
	functions	0.5000	0.6139	−0.0740	0.8771
	lazy	0.6447	0.6447	0.2756	0.6325
	meta	0.7559	0.7559	0.3786	0.8864
	misc	0.7559	0.6139	0.1384	0.6447
	rules	0.3430	0.7559	−0.0783	0.6447
	trees	0.5000	0.7559	0.1258	1.0000
	overall	0.7559	0.7559	0.1432	1.0000
OV-mRNA And OV-miRNA	bayes	0.4725	0.4725	0.1863	0.8864
	functions	−1.0000	0.7500	−0.0815	0.8771
	lazy	0.7500	0.8771	0.2042	0.6447
	meta	0.3162	0.3162	0.2854	0.8864
	misc	0.6139	0.7500	0.2408	0.8864
	rules	0.3430	0.3162	−0.0971	0.8864
	trees	0.3162	0.4725	0.1496	1.0000
	overall	0.3162	0.4725	0.1161	1.0000

All of the measurements in the tables are MCCs, and the vote after filtering is the MCC based on the eliminated methods. The “avg” is the average of the MCCs.