Classification Based on Pruning and Double Covered Rule Sets for the Internet of Things Applications

Table 2

The accuracy of ID3, FOIL, CVCR, CDCR, and CDCR-P.

Dataset	ID3	FOIL	CVCR	CDCR	CDCR-P
Balance	0.3716	0.4929	0.7810	0.7985	0.8289
Breast	0.9042	0.9342	0.9571	0.9556	0.9585
Car	0.7298	0.7714	0.8837	0.9248	0.8767
Lymph	0.7148	0.7424	0.8181	0.81	0.81
Monks	0.9448	0.8146	0.8959	0.9514	0.9537
Mushroom	0.985	0.995	0.99	0.99	0.99
Soybean	0.4102	0.4172	0.8180	0.8276	0.8601
SPECT	0.7181	0.752	0.7303	0.7453	0.8053
Tic-tac	0.8215	0.9875	0.8684	0.9541	0.9530
Zoo	0.97	0.9409	0.9709	0.9609	0.8918
Cleve	0.7426	0.7423	0.8152	0.8216	0.8482
Heart	0.8148	0.8148	0.7556	0.7852	0.7963
Iris	0.7733	0.9533	0.8133	0.8133	0.8533
Wine	0.96	0.9379	0.983	0.9712	0.9882
Average	0.7758	0.8069	0.8629	0.8793	0.8867