Classifying Human Voices by Using Hybrid SFX Time-Series Preprocessing and Ensemble Feature Selection

Table 5

Optimal FS methods for each dataset.

FS accuracy %	FM				ES				SI				LR
Feature selection method	CFS	ChiS	MRMR	WSA	CFS	ChiS	MRMR	WSA	CFS	ChiS	MRMR	WSA	CFS	ChiS	MRMR	WSA
Original no. of attributes	74	74	74	74	68	68	68	68	88	88	88	88	75	75	75	75
No. of attributes after FS	8	55	30	20	17	53	30	21	20	71	30	29	32	32	30	31
Classification algorithm
J48	63.1783	64.7287	63.5659	67.4419	61.25	59.375	57.5	64.375	73.1618	66.1765	74.6324	64.7059	94	92.3333	70	94
BFTree	71.7054	68.9922	62.0155	76.3566	58.125	56.25	65	65.625	83.0882	79.7794	85.6618	81.25	93.3333	91.6666	68	93.3333
FT	74.031	79.0698	72.8682	82.5581	64.375	78.125	65.625	88.75	97.4265	88.9706	93.75	88.2353	72.6667	71	69.3333	76
LMT	63.5659	92.4419	73.6434	97.6744	60.625	83.125	70	88.125	96.3235	92.4465	91.9118	87.8676	86.6667	85	66.6667	86.6667
NBTree	71.7054	63.1783	63.1783	70.155	51.25	59.375	67.5	66.25	n/a	n/a	n/a	n/a	67.3333	65.6666	66.6667	68
RandomForest	73.6434	70.5426	67.8295	72.8682	61.875	74.375	71.25	73.125	90.4412	74.2647	81.9853	81.9853	90.6667	89	70.6667	90.6667
RandomTree	64.7287	59.6899	55.814	70.155	45	66.25	57.5	67.5	69.1176	61.7647	69.4853	63.9706	85.3333	83.6666	84.6667	85.3333
REPTree	72.093	72.093	67.4419	73.6434	56.875	61.25	60	64.375	84.9265	84.1912	79.4118	83.4559	94.6667	93	66.6667	94.6667
ConjunctiveRule	55.814	55.814	48.8372	65.8915	54.375	55	55	56.25	n/a	n/a	n/a	n/a	66.6667	65	65.3333	66.6667
DecisionTable	70.155	70.155	53.876	68.6047	57.5	56.25	61.875	52.5	67.2794	58.8235	63.2353	59.5588	93.3333	91.6666	77.3333	93.3333
FURIA	71.7054	77.1318	63.9535	74.8062	62.5	68.75	64.375	53.125	80.5147	62.5	78.6765	86.3971	84	82.3333	70	84
JRip	72.8682	71.7054	67.8295	73.6434	66.25	54.375	58.125	55	74.2647	34.9265	72.0588	65.4412	98	96.3333	66.6667	98
NNge	68.9922	66.6667	55.814	63.5659	53.125	46.25	52.5	50.625	92.6471	91.1765	92.2794	81.25	87.3333	85.6666	69.3333	87.3333
OneR	50.3876	50.3876	54.2636	54.2636	54.375	54.375	54.375	61.25	55.1471	55.1471	49.2647	49.2647	93.3333	91.6666	54	93.3333
PART	64.3411	68.6047	62.4031	68.6047	58.75	56.875	58.125	70.625	73.5294	67.2794	80.5147	71.3235	94	92.3333	69.3333	94
NaiveBayes	70.9302	64.7287	67.4419	67.0543	64.375	68.75	58.75	58.75	95.2206	76.8382	86.0294	72.0588	78.6667	77	64	78.6667
Bagging	73.2558	75.1938	70.155	75.969	63.75	64.375	68.75	53.125	89.3382	85.6618	84.9265	86.3971	93.3333	91.6666	66.6667	94.6667
LibSVM	66.6667	87.5969	63.5659	89.9225	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	57.3333	55.6666	46.6667	57.3333
MultilayerPerceptron	68.6047	79.0698	72.4806	89.5349	63.75	70.625	61.875	66.875	91.9118	93.75	93.75	86.3971	100	98.3333	70	100
SMO	72.4806	77.907	73.6434	79.4574	76.875	75.625	61.875	67.5	93.0147	93.75	93.3824	80.5147	98	96.3333	76	99.3333
Mean accuracy %	68.04263	70.78489	64.03102	74.108535	59.73684	63.65132	61.57895	64.40789	82.78547	74.55568	80.64448	75.88668	86.43333	84.76663	67.90001	86.76667
Time (s)	0.78	2.867	3.56	31.275	1.03	3.328	1.439	441.476	1.91	3.815	3.26	3585	1.39	2.17	4.8	906