A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

Table 6

The results for financial distress dataset.

Feature selection	Classifier	Accuracy	Type I	Type II
Linear
Chi-square	GEP	98.96	0.010916	0.009292
	Decision tree	98.85	0.01279	0.008168
	MLP	95.35	0.043108	0.054842
	SVM	97.94	0.009018	0.051282
	RBF	77.43	0.727498	0.018755
	KNN	87.57	0.225761	0.078842
Decision tree	GEP	99.06	0.009967	0.00813
	Decision tree	98.98	0.011369	0.007001
	MLP	97.43	0.024633	0.028005
	SVM	97.91	0.008543	0.051103
	RBF	75.21	0.838425	0.010161
	KNN	89.61	0.218475	0.057228
KNN	GEP	98.96	0.011391	0.00813
	Decision tree	98.95	0.011369	0.008168
	MLP	97.16	0.002369	0.092182
	SVM	95.18	0.060161	0.018673
	RBF	82.42	0.598425	0.048435
	KNN	98.14	0.025846	0.085834
LDA	GEP	98.96	0.011391	0.00813
	Decision tree	98.95	0.011369	0.008168
	MLP	96.52	0.001895	0.115519
	SVM	98.48	0.012228	0.032914
	RBF	85.14	0.493883	0.012481
	KNN	97.75	0.039478	0.013478
Logistic	GEP	99.02	0.011391	0.005807
	Decision tree	98.75	0.012912	0.011453
	MLP	97.60	0.002842	0.075846
	SVM	97.97	0.018475	0.024504
	RBF	95.17	0.147651	0.012779
	KNN	98.03	0.038432	0.013497
Nonlinear
MLP	GEP	98.25	0.041766	0.034843
	Decision tree	97.87	0.006158	0.058343
	MLP	98.04	0.007106	0.050175
	SVM	94.64	0.009000	0.163361
	RBF	97.49	0.058742	0.012411
	KNN	97.09	0.052413	0.018754
Naive Bayes	GEP	98.52	0.010916	0.024390
	Decision tree	97.97	0.006158	0.054842
	MLP	95.78	0.040739	0.045508
	SVM	94.81	0.005685	0.165694
	RBF	88.58	0.283871	0.052137
	KNN	86.37	0.262475	0.092571
RBF network	GEP	98.99	0.00813	0.010916
	Decision tree	98.95	0.011369	0.008168
	MLP	91.00	0.000474	0.310385
	SVM	96.36	0.039792	0.028005
	RBF	97.70	0.048742	0.012378
	KNN	97.97	0.028773	0.012475
Rough set	GEP	99.02	0.010916	0.006969
	Decision tree	98.88	0.01279	0.007001
	MLP	93.93	0.074372	0.026838
	SVM	97.91	0.008543	0.051103
	RBF	78.07	0.728475	0.013418
	KNN	88.48	0.217582	0.073591
SVM	GEP	98.92	0.010067	0.012941
	Decision tree	98.85	0.011369	0.011669
	MLP	73.28	0.03837	0.169195
	SVM	98.11	0.016106	0.025671
	RBF	73.10	0.884217	0.023458
	KNN	69.13	0.592195	0.201728
Linear
Join	GEP	98.96	0.010916	0.009292
	Decision tree	98.95	0.011369	0.008168
	MLP	97.20	0.002369	0.091015
	SVM	96.09	0.044529	0.025671
	RBF	78.09	0.724258	0.022889
	KNN	86.32	0.262479	0.091348
	Average	92.60	0.1759866	0.041397
Disjoin	GEP	98.99	0.010916	0.00813
	Decision tree	98.88	0.01279	0.007001
	MLP	92.38	0.085741	0.052509
	SVM	98.15	0.008068	0.027944
	RBF	77.43	0.732561	0.023457
	KNN	87.57	0.262877	0.083271
	Average	92.23	0.185492	0.033718
Nonlinear
Join	GEP	98.96	0.011391	0.00813
	Decision tree	98.95	0.011369	0.008168
	MLP	97.16	0.002369	0.092182
	SVM	95.18	0.060161	0.01867
	RBF	82.42	0.60247	0.002431
	KNN	98.14	0.034237	0.012798
	Average	95.14	0.120332	0.023723
Disjoin	GEP	98.96	0.010916	0.009292
	Decision tree	98.85	0.012808	0.008168
	MLP	95.30	0.043108	0.054842
	SVM	97.94	0.009018	0.04878
	RBF	77.11	0.763741	0.012885
	KNN	86.94	0.242298	0.083179
	Average	92.51	0.180314	0.036191

Note. denotes the best result in accuracy, Type I, and Type II, respectively. denotes the better result for average of join and disjoin in accuracy, Type I, and Type II, respectively.