AdaBoost-based multiple SVM-RFE for classification of mammograms in DDSM

Table 7 Comparison of methods by maximum Az value using 22 features (Mass)

	T	MGH	WU	WFUSM	SHH
SVM		0.88805	0.93642	0.92474	0.94998
SVM-RFE		0.88849	0.94173	0.93037	0.94998
ENSEMBLE		0.81490	0.90299	0.80317	0.86155
JOIN (1)		0.86728	0.92278	0.87638	0.90789
JOIN (2)		0.83034	0.93886	0.89597	0.85132
JOIN (3)		0.75098	0.87312	0.82694	0.83834
JOIN (4)		0.74270	0.74262	0.66948	0.83834
JOIN (5)		0.68776	0.71316	0.66948	0.80802
MSVM-RFE (bootstrap)	5	0.89720	0.93729	0.92664	0.95087
	10	0.88833	0.93666	0.92972	0.95016
	15	0.89920	0.93746	0.93000	0.95076
	20	0.89014	0.94290	0.92986	0.95066
MSVM-RFE (boost)	5	0.88993	0.93987	0.93581	0.94998
	10	0.88805	0.94315	0.92812	0.94998
	15	0.89092	0.94204	0.92789	0.94998
	20	0.88805	0.94197	0.92758	0.95245