Journal of Big Data

Table 5 AUC values for multi-class data set (LVH)

From: Improved classification of large imbalanced data sets using rationalized technique: Updated Class Purity Maximization Over_Sampling Technique (UCPMOT)

Classifier	Data set	Over_sampling techniques
Classifier	Data set	A	B	C	D	E	F	G	H	I	J	K
Random Forest	Yeast	0.67	0.74	0.76	0.75	0.82	0.74	0.83	0.84	0.85	0.85	0.77
	Car	0.90	0.93	0.94	0.94	0.94	0.93	0.95	0.96	0.96	0.96	0.93
	KEGG-U	0.87	0.90	0.91	0.90	0.92	0.89	0.93	0.93	0.95	0.94	0.92
Naïve Bayes	Yeast	0.62	0.68	0.71	0.70	0.79	0.69	0.80	0.79	0.81	0.80	0.76
	Car	0.86	0.89	0.90	0.90	0.91	0.89	0.92	0.92	0.94	0.93	0.90
	KEGG-U	0.82	0.85	0.86	0.86	0.88	0.85	0.89	0.89	0.90	0.88	0.87
AdaBoostM1	Yeast	0.65	0.72	0.75	0.74	0.81	0.73	0.82	0.82	0.83	0.82	0.76
	Car	0.89	0.92	0.93	0.93	0.93	0.92	0.94	0.95	0.95	0.94	0.93
	KEGG-U	0.85	0.88	0.89	0.89	0.91	0.88	0.91	0.91	0.92	0.90	0.90
MultiLayer Perceptron	Yeast	0.64	0.70	0.73	0.72	0.80	0.71	0.81	0.81	0.82	0.81	0.78
	Car	0.88	0.90	0.92	0.91	0.92	0.90	0.94	0.94	0.96	0.94	0.92
	KEGG-U	0.84	0.87	0.88	0.88	0.90	0.87	0.91	0.90	0.91	0.89	0.88
Overall average		0.79	0.83	0.84	0.84	0.87	0.83	0.88	0.88	0.9	0.88	0.86

Italic values indicate highest result

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