An Amalgamated Approach to Bilevel Feature Selection Techniques Utilizing Soft Computing Methods for Classifying Colon Cancer

Table 5

Performance analysis of classifiers in terms of GDR with six optimization techniques for different gene selection methods using 30-60 – 90 selected genes.

Classifiers	Gene selection	Optimization techniques
		Invasive Weed Optimization	Teaching Learning-Based Optimization	League Championship Optimization	Beetle Antennae Search Optimization	Crow Search Optimization	Fruit fly Optimization
RF	30	71.48527	85.71429	79.69	52.27	52.27	59.89258
	60	70.44469	86.39268	86.72	51.625	78.38813	74.96797
	90	51.6875	63.80399	79.17	52	57.42563	85.02938
Adaboost	30	54.54545	71.35536	82.295	51.5	51.125	79.93242
	60	88.0225	87.11	91.15	55.00906	13.1496	71.875
	90	84.38	65.88466	71.48527	9.423984	51.25	77.24688
LR	30	61.72	80.99	89.0675	16.68824	20.34483	86.98345
	60	12.55858	73.50344	80.21	9.546483	4.758999	97.32657
	90	71.1444	73.68953	13.91238	86.46	71.09555	88.07165
DT	30	94.79474	54.10938	18.42734	91.41	84.38	88.47734
	60	90.29073	78.38813	23.85838	55.12336	91.15	88.47734
	90	59.38	56.90313	89.59	87.5	95.31715	65.73803
QDA	30	52.7425	54.16	87.11	72.65625	84.68635	95.83958
	60	77.085	83.86	87.06308	54.10938	73.3201	91.67
	90	98.96	84.38	84.38	52.135	82.9582	91.40911
Average		69.28276	73.34964	70.94193	53.16378	60.77463	82.86249