Table 3.

Comparison of our proposed method (LUCCK) with other machine learning methods in terms of accuracy and running time, averaged over 10 folds.

Dataset	Method	Accuracy (%)	Time (s)
Sonar (208 samples)	LUCCK	87.42	1.5082
	3-NN	81.66	0.0178
	5-NN	81.05	0.0178
	Adaboost	82.19	1.0239
	SVM	81.00	0.0398
	Random Forest (10)	78.14	0.1252
	Random Forest (100)	83.39	1.1286
	LDA	74.90	0.0343
Glass (214 samples)	LUCCK	82.56	0.3500
	3-NN	68.72	0.0161
	5-NN	67.04	0.0162
	Adaboost	50.82	0.5572
	SVM	35.57	0.0342
	Random Forest (10)	75.31	0.1062
	Random Forest (100)	79.24	0.9319
	LDA	63.28	0.0155
Iris (150 samples)	LUCCK	95.93	0.1508
	3-NN	96.09	0.0135
	5-NN	96.54	0.0135
	Adaboost	93.82	0.4912
	SVM	96.52	0.0143
	Random Forest (10)	94.81	0.0889
	Random Forest (100)	95.29	0.7686
	LDA	98.00	0.0122
E. coli (336 samples)	LUCCK	87.61	0.5937
	3-NN	85.08	0.0190
	5-NN	86.43	0.0193
	Adaboost	74.13	0.6058
	SVM	87.53	0.0448
	Random Forest (10)	84.56	0.1075
	Random Forest (100)	87.34	0.9265
	LDA	81.46	0.0182