Table 3.

Classification results with Breast Cancer dataset.

Methodology adopted	Accuracy (%)	Sensitivity (%)	Specificity (%)	Selected features
Optimized LVQ (10x CV) [10]	96.70	91.29	92.34	2, 3, 6
Big LVQ (10x CV) [10]	96.80	95.23	96.10	2, 3, 6
AIRS (10x CV) [10]	97.20	96.92	95.00	2, 3, 6, 7
Supervised fuzzy clustering (10x CV) [11]	95.57	98.23	97.36	2, 3, 6, 7, 8
Fuzzy-AIS-knn (10x CV) [12]	99.14	99.56	100	2, 3, 6, 7, 8
F-score + support vector machine [13]	99.51	99.24	98.61	2, 3, 6, 7
Association rule + neural network [14]	97.4	93.12	91.26	2, 3, 6, 7, 8
Artificial metaplasticity neural network [15]	99.26	100	97.89	2, 3, 6, 7, 8
Mean selection method [16]	95.99	93	97	2, 3, 6, 7
Half selection method [16]	96.71	94	98	2, 3, 6, 7, 8
Neural network for threshold selection [16]	97.28	94	99	1, 2, 3, 5, 6, 7, 8
PSO + ELM	99.62	99.61	98.93	2, 3, 6, 7, 8
Proposed SRLPSO + ELM	99.78	100	100	2, 3, 6, 7