Table 7. mycoSORT Results - Set of Features F5.

Under-sampling(USF)	Classifier	Precision	Recall	F-measure	MCC	F-2
Training set with USF 0%	Naive Bayes	0.307	0.720	0.430	0.382	0.570
Training set with USF 0%	LMT	0.656	0.420	0.512	0.485	0.450
Training set with USF 0%	LibSVM	0.833	0.033	0.064	0.155	0.040
Training set with USF 5%	Naive Bayes	0.310	0.733	0.436	0.390	0.580
Training set with USF 5%	LMT	0.600	0.500	0.545	0.503	0.520
Training set with USF 5%	LibSVM	0.703	0.173	0.278	0.319	0.200
Training set with USF 10%	Naive Bayes	0.307	0.760	0.438	0.396	0.590
Training set with USF 10%	LMT	0.574	0.567	0.570	0.523	0.570
Training set with USF 10%	LibSVM	0.704	0.333	0.452	0.449	0.370
Training set with USF 15%	Naive Bayes	0.309	0.793	0.445	0.41	0.600
Training set with USF 15%	LMT	0.458	0.693	0.552	0.504	0.630
Training set with USF 15%	LibSVM	0.596	0.413	0.488	0.451	0.440
Training set with USF 20%	Naive Bayes	0.314	0.793	0.450	0.415	0.610
Training set with USF 20%	LMT	0.422	0.653	0.513	0.460	0.590
Training set with USF 20%	LibSVM	0.545	0.527	0.536	0.485	0.530
Training set with USF 25%	Naive Bayes	0.312	0.780	0.446	0.408	0.600
Training set with USF 25%	LMT	0.399	0.673	0.501	0.449	0.590
Training set with USF 25%	LibSVM	0.481	0.580	0.526	0.470	0.560
Training set with USF 30%	Naive Bayes	0.288	0.767	0.418	0.377	0.580
Training set with USF 30%	LMT	0.388	0.727	0.506	0.461	0.620
Training set with USF 30%	LibSVM	0.460	0.687	0.551	0.503	0.630
Training set with USF 35%	Naive Bayes	0.302	0.780	0.435	0.397	0.590
Training set with USF 35%	LMT	0.359	0.807	0.497	0.465	0.650
Training set with USF 35%	LibSVM	0.369	0.800	0.505	0.472	0.650
Training set with USF 40%	Naive Bayes	0.303	0.773	0.435	0.396	0.590
Training set with USF 40%	LMT	0.344	0.840	0.488	0.463	0.650
Training set with USF 40%	LibSVM	0.338	0.840	0.482	0.456	0.650

Results of Positive Class on Feature Setting #3, Using Only Bag-of-Words as Features.