Adaptive Particle Swarm Optimization Algorithm Ensemble Model Applied to Classification of Unbalanced Data

Table 5

Results of the measured performance of models.

Data	Model	ACC	Precision	Recall	F1-measure	ROC area
ecoli-3	LR	0.922	0.917	0.922	0.910	0.910
	MLP	0.932	0.933	0.934	0.933	0.932
	DT	0.922	0.915	0.922	0.917	0.824
	SVM	0.896	0.802	0.896	0.846	0.502
	RF	0.934	0.929	0.934	0.932	0.936
	APSO-RF	0.938	0.931	0.942	0.941	0.939
glass-1	LR	0.648	0.621	0.648	0.618	0.681
	MLP	0.662	0.668	0.662	0.665	0.676
	DT	0.775	0.770	0.775	0.771	0.749
	SVM	0.793	0.794	0.793	0.784	0.743
	RF	0.836	0.837	0.836	0.824	0.896
	APSO-RF	0.851	0.841	0.838	0.830	0.902
new-thyroid-1	LR	0.986	0.986	0.986	0.986	0.997
	MLP	0.981	0.981	0.980	0.981	0.997
	DT	0.981	0.981	0.981	0.981	0.972
	SVM	0.879	0.894	0.879	0.847	0.629
	RF	0.972	0.972	0.972	0.971	0.998
	APSO-RF	0.988	0.987	0.986	0.982	0.998
page-blocks-0	LR	0.951	0.947	0.950	0.947	0.941
	MLP	0.968	0.967	0.968	0.967	0.978
	DT	0.986	0.986	0.986	0.986	0.991
	SVM	0.994	0.992	0.990	0.994	0.977
	RF	0.996	0.995	0.992	0.996	0.993
	APSO-RF	0.997	0.997	0.994	0.998	0.994
vehicle-1	LR	0.786	0.781	0.786	0.783	0.937
	MLP	0.842	0.838	0.840	0.839	0.918
	DT	0.717	0.714	0.717	0.716	0.830
	SVM	0.492	0.242	0.492	0.325	0.502
	RF	0.831	0.803	0.812	0.821	0.933
	APSO-RF	0.852	0.840	0.832	0.842	0.994
wisconsin	LR	0.965	0.965	0.965	0.965	0.992
	MLP	0.963	0.963	0.963	0.963	0.992
	DT	0.959	0.959	0.959	0.959	0.957
	SVM	0.960	0.964	0.960	0.961	0.968
	RF	0.969	0.969	0.969	0.969	0.993
	APSO-RF	0.971	0.975	0.974	0.975	0.944
veast-1	LR	0.757	0.740	0.757	0.728	0.790
	MLP	0.769	0.756	0.769	0.757	0.796
	DT	0.760	0.745	0.760	0.746	0.726
	SVM	0.721	0.713	0.721	0.626	0.526
	RF	0.778	0.767	0.778	0.769	0.806
	APSO-RF	0.782	0.774	0.782	0.770	0.821