Clustering Ensemble Technology Based on Granular Computing to Extract Cervical Cancer Predictors

Table 4

Classification accuracy of four clustering methods using three classifiers on six datasets.

Dataset		GSE6791	GSE7803	GSE9750	GSE63514	GSE52903	GSE29570
Classifier	Method
DTree	AA	0.67	0.86	0.83	0.73	0.8	0.77
	AB	1	0.71	0.83	0.73	0.8	0.46
	CA	1	0.86	0.83	0.91	0.93	0.85
	CAPA	0.83	1	0.5	0.64	0.73	0.92
Forest	AA	0.83	0.86	0.75	0.82	0.8	0.85
	AB	1	0.71	0.83	0.64	0.8	0.69
	CA	1	1	1	0.91	0.93	0.92
	CAPA	0.83	0.71	0.92	0.82	0.8	0.69
SVM	AA	0.83	1	0.92	0.82	1	0.85
	AB	1	0.71	0.83	0.45	0.8	0.69
	CA	1	1	1	0.82	0.8	0.85
	CAPA	0.83	0.71	0.83	0.73	0.8	0.69

The number “1” corresponding to the AB method in each classifier should be marked in italics in the GSE6791 column of data.