Journal of Big Data

Table 6 AUC values for multi-class data set (OVA)

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	0.75	0.77	0.76	0.84	0.75	0.85	0.86	0.87	0.86	0.79
	Car	0.90	0.93	0.95	0.94	0.96	0.93	0.96	0.97	0.97	0.96	0.95
	KEGG-U	0.87	0.92	0.93	0.93	0.94	0.92	0.94	0.94	0.96	0.94	0.93
Naïve Bayes	Yeast	0.63	0.71	0.73	0.72	0.80	0.71	0.81	0.82	0.83	0.81	0.75
	Car	0.87	0.90	0.91	0.90	0.93	0.90	0.93	0.93	0.94	0.93	0.92
	KEGG-U	0.83	0.87	0.89	0.89	0.89	0.88	0.90	0.90	0.91	0.89	0.88
AdaBoostM1	Yeast	0.65	0.73	0.75	0.74	0.82	0.74	0.83	0.84	0.85	0.83	0.77
	Car	0.89	0.92	0.93	0.93	0.95	0.92	0.95	0.96	0.96	0.95	0.94
	KEGG-U	0.85	0.90	0.91	0.91	0.92	0.90	0.92	0.92	0.94	0.92	0.91
MultiLayer Perceptron	Yeast	0.64	0.72	0.74	0.73	0.81	0.72	0.82	0.83	0.84	0.82	0.76
	Car	0.88	0.91	0.92	0.91	0.94	0.91	0.94	0.94	0.95	0.94	0.93
	KEGG-U	0.84	0.88	0.90	0.90	0.90	0.89	0.91	0.91	0.92	0.90	0.89
Overall average		0.79	0.84	0.86	0.85	0.89	0.84	0.89	0.9	0.91	0.89	0.86

Italic values indicate highest result

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