Random generalized linear model: a highly accurate and interpretable ensemble predictor

BMC Bioinformatics

Table 4 Prediction accuracy in the 20 disease gene expression data sets

Data set	RGLM	RF	RFbigmtry	Rpart	LDA	DLDA	KNN	SVM	SC
adenocarcinoma	0.842	0.842	0.842	0.737	0.842	0.744	0.842	0.842	0.803
brain	0.881	0.810	0.833	0.762	0.810	0.929	0.881	0.786	0.929
breast2	0.623	0.610	0.636	0.584	0.610	0.636	0.584	0.558	0.636
breast3	0.705	0.695	0.716	0.611	0.695	0.705	0.669	0.674	0.700
colon	0.855	0.823	0.823	0.726	0.855	0.839	0.774	0.774	0.871
leukemia	0.921	0.895	0.921	0.816	0.868	0.974	0.974	0.763	0.974
lymphoma	0.968	1.000	1.000	0.903	0.960	0.984	0.984	1.000	0.984
NCI60	0.902	0.869	0.869	0.738	0.885	0.902	0.852	0.869	0.918
prostate	0.931	0.892	0.902	0.853	0.873	0.627	0.804	0.853	0.912
srbct	1.000	0.944	0.984	0.921	0.857	0.905	0.952	0.873	1.000
BrainTumor2	0.760	0.750	0.740	0.620	0.760	0.700	0.700	0.660	0.720
DLBCL	0.909	0.851	0.883	0.831	0.922	0.779	0.870	0.792	0.857
lung1	0.931	0.931	0.931	0.828	0.914	0.931	0.931	0.897	0.914
lung2	0.935	0.935	0.935	0.826	0.957	0.978	0.935	0.848	0.978
lung3	0.901	0.901	0.887	0.803	0.873	0.859	0.831	0.859	0.887
psoriasis1	0.989	0.994	0.989	0.978	0.994	0.989	0.989	0.983	0.989
psoriasis2	0.963	0.988	0.976	0.963	0.976	0.963	0.963	0.963	0.963
MSstage1	0.846	0.846	0.846	0.423	0.769	0.769	0.808	0.769	0.769
MSdiagnosis1	0.963	0.926	0.926	0.556	0.889	0.889	0.963	0.926	0.926
MSdiagnosis2	0.591	0.614	0.614	0.568	0.545	0.568	0.568	0.568	0.523
MeanAccuracy	0.871	0.856	0.863	0.752	0.843	0.833	0.844	0.813	0.863
Rank	1	4	2.5	9	6	7	5	8	2.5
Pvalue	NA	0.029	0.079	0.00014	0.0075	0.05	0.014	0.00042	0.37

For each data set, the prediction accuracy was estimated using 3−f o l d cross validation across 100 random partitions of the data into 3 folds. Mean accuracies across the 20 data sets and the resulting ranks are summarized at the bottom. Two sided paired Wilcoxon test p-values can be used to determine whether the accuracy of RGLM is significantly different from that of other predictors. Note that the RGLM yields the highest mean accuracy.

ISSN: 1471-2105