CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests

Table 8 The multi-classification results

		1	\( \left\lfloor \sqrt{M}\right\rfloor \)	⌊ log₂(M) + 1⌋	M	GA-RF	PSO-RF	AFSA-RF
Steel Plates Faults	Accuracy	0.7464	0.7485	0.7598	0.7814	0.7881	0.7998	0.7914
	OOB	0.3152	0.2819	0.2746	0.2640	0.2437	0.2276	0.2115
	margin	0.2456	0.3384	0.3484	0.3789	0.3803	0.3812	0.3810
	nTree	100	100	100	100	397	283	400
	κ	1	5	5	27	8	6	6
	num (Attribute)	27	27	27	27	23	22	22
Libras Movement	Accuracy	0.7167	0.7556	0.6889	0.6444	0.7606	0.7767	0.7928
	OOB	0.3546	0.3397	0.3480	0.3163	0.3030	0.3323	0.3116
	margin	0.1464	0.1798	0.1990	0.2180	0.2443	0.2677	0.2910
	nTree	100	100	100	100	258	348	135
	κ	1	9	7	90	12	8	9
	num (Attribute)	90	90	90	90	56	76	49
mfeat-fac	Accuracy	0.4280	0.9030	0.8010	0.9620	0.9673	0.9600	0.9611
	OOB	0.6949	0.1823	0.3192	0.0486	0.0416	0.0410	0.0361
	margin	−0.0987	0.4561	0.2361	0.8708	0.8749	0.8615	0.8698
	nTree	100	100	100	100	377	270	196
	κ	1	15	8	215	14	18	11
	num (Attribute)	215	215	215	215	145	112	164

The best value of every performance evaluation criteria obtained by the algorithms are marked in boldface

ISSN: 1471-2105