“Noisy beets”: impact of phenotyping errors on genomic predictions for binary traits in Beta vulgaris

Biscarini, Filippo; Nazzicari, Nelson; Broccanello, Chiara; Stevanato, Piergiorgio; Marini, Simone

doi:10.1186/s13007-016-0136-4

Plant Methods

Table 1 Total classification error (TER), false negative (FNR) and false positive (FPR) rates, and area under the ROC curve (AUC) for increasing proportions of mislabeled observations with the five tested classification models

From: “Noisy beets”: impact of phenotyping errors on genomic predictions for binary traits in Beta vulgaris

misLabels (%)	minFreq	errType	KNN	LR	RF	SVM-Lin	SVM-Rbf
0	0.1950	TER	0.0000	0.0000	0.0085	0.0000	0.0001
		FNR	0.0000	0.0000	0.0067	0.0000	0.0001
		FPR	0.0020	0.0020	0.0054	0.0020	0.0036
		AUC	1.0000	0.9980	0.9946	0.9980	0.9961
1	0.1870	TER	0.0039	0.0153	0.0092	0.0077	0.0008
		FNR	0.0042	0.0153	0.0076	0.0078	0.0007
		FPR	0.0038	0.0046	0.0036	0.0095	0.0044
		AUC	0.9961	0.9954	0.9964	0.9905	0.9955
2.5	0.1870	TER	0.0045	0.0291	0.0102	0.0145	0.0004
		FNR	0.0049	0.0283	0.0094	0.0139	0.0004
		FPR	0.0041	0.0094	0.0032	0.0174	0.0023
		AUC	0.9959	0.9905	0.9968	0.9825	0.9977
5	0.2114	TER	0.0088	0.0897	0.0236	0.0471	0.0047
		FNR	0.0096	0.0864	0.0213	0.0466	0.0043
		FPR	0.0052	0.0484	0.0052	0.0496	0.0073
		AUC	0.9948	0.9516	0.9951	0.9503	0.9918
7.5	0.2520	TER	0.0160	0.1431	0.0342	0.0708	0.0087
		FNR	0.0159	0.1386	0.0307	0.0688	0.0077
		FPR	0.0071	0.0920	0.0077	0.0748	0.0116
		AUC	0.9928	0.9080	0.9921	0.9251	0.9882
10	0.2439	TER	0.0292	0.2011	0.0553	0.1111	0.0205
		FNR	0.0294	0.1963	0.0521	0.1105	0.0188
		FPR	0.0100	0.1462	0.0173	0.1134	0.0242
		AUC	0.9898	0.8538	0.9827	0.8866	0.9754
12.5	0.2846	TER	0.0396	0.2286	0.0679	0.1275	0.0328
		FNR	0.0393	0.2247	0.0625	0.1297	0.0285
		FPR	0.0139	0.1680	0.0214	0.1277	0.0381
		AUC	0.9861	0.8320	0.9786	0.8723	0.9614
15	0.2927	TER	0.0536	0.2714	0.0924	0.1687	0.0484
		FNR	0.0533	0.2637	0.0867	0.1687	0.0439
		FPR	0.0254	0.2237	0.0358	0.1705	0.0535
		AUC	0.9746	0.7763	0.9642	0.8292	0.9460
17.5	0.2764	TER	0.0691	0.2903	0.1098	0.1887	0.0635
		FNR	0.0692	0.2867	0.1017	0.1889	0.0595
		FPR	0.0323	0.2425	0.0549	0.1903	0.0686
		AUC	0.9677	0.7575	0.9451	0.8097	0.9091
20	0.2846	TER	0.0924	0.3095	0.1258	0.2166	0.0835
		FNR	0.0948	0.3068	0.1207	0.2212	0.0767
		FPR	0.0402	0.2608	0.0594	0.2149	0.0906
		AUC	0.9598	0.7391	0.9406	0.7851	0.9081
25	0.3984	TER	0.1334	0.3415	0.1947	0.2550	0.1377
		FNR	0.1325	0.3344	0.1829	0.2582	0.1141
		FPR	0.0800	0.2976	0.1320	0.2559	0.1454
		AUC	0.9198	0.7024	0.8680	0.7441	0.8532
30	0.3659	TER	0.2073	0.3693	0.2522	0.3079	0.1989
		FNR	0.2079	0.3700	0.2477	0.3156	0.1745
		FPR	0.1518	0.3439	0.1930	0.3067	0.2099
		AUC	0.8481	0.6561	0.8069	0.6933	0.7901
40	0.4309	TER	0.3681	0.4382	0.3884	0.4044	0.3551
		FNR	0.3723	0.4376	0.3916	0.4087	0.3088
		FPR	0.3254	0.4223	0.3546	0.4051	0.3639
		AUC	0.6745	0.5777	0.6453	0.5949	0.6351
50	0.5203	TER	0.5111	0.5134	0.5194	0.5130	0.5116
		FNR	0.5214	0.5120	0.5199	0.5161	0.5238
		FPR	0.5208	0.5165	0.5170	0.5147	0.5137
		AUC	0.4792	0.4834	0.4830	0.4853	0.4862

Reported values of classification performance are average validation results from a 5-fold cross-validation scheme repeated 100 times (per model, per mislabel proportion). MinFreq is the frequency of the minority class (low-root vigor). In italic the best performing method (in terms of AUC) for each percentage of noisy lables
KNN K-nearest neighbours, LR ridge logistic regression, RF random forest, SVM-Lin SVM with linear kernel, SVM-Rbf SVM with radial basis function

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ISSN: 1746-4811

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