Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification

Table 3

Comparative analysis of FSS-OANFIS technique with existing approaches.

Methods	Accuracy	Sensitivity	Specificity	G-measure
Leukemia dataset
FSS-OANFIS	83.33	75.00	75.00	80.08
AHSA-GS	75.49	69.66	74.81	45.94
PSO algorithm	80.59	74.95	73.96	68.07
DE algorithm	68.67	63.01	63.62	64.80
Prostate dataset
FSS-OANFIS	80.65	80.00	80.00	81.37
AHSA-GS	71.19	53.82	79.79	79.84
PSO algorithm	68.78	63.63	70.15	66.01
DE algorithm	62.77	60.37	63.22	67.94
Stanford dataset
FSS-OANFIS	73.33	66.67	66.67	70.47
AHSA-GS	71.27	62.64	65.15	68.82
PSO algorithm	72.80	60.82	60.17	61.74
DE algorithm	66.93	63.80	59.16	61.48
Colon dataset
FSS-OANFIS	89.47	87.82	87.82	87.82
AHSA-GS	61.02	48.07	64.04	43.62
PSO algorithm	59.00	43.02	58.34	36.66
DE algorithm	50.38	33.63	38.76	58.07