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Intelligent systems in accounting, finance and managementassessing predictive performance of ann-based classifiers, страница 19

Laplace. Our first hypothesis (H1) is satisfied (Table XVII), i.e. GA performing better than RT in refining the solution. However, the difference between accuracy rates is not as obvious as it was for the ‘real’ data from experiment 1. This is explainable, since in the latter case (only ‘real’ data) the starting solution has relatively low accuracy rates (80–90%) and it could have been easily improved, whereas in this experiment (centralized data) we have some starting solutions with high accuracy rates (95–98%) that would be difficult to improve whatever the training mechanism used

Table XIII. Three-way ANOVA for training

Source	Type III sum of squares	DF	Mean square	F	Sig.	Partial η²
Model	979 340.512	30	32 644.684	6 322 228.263	0.000	1.000
PREPROC	540.706	2	270.353	52 358.680	0.000	0.999
DISTRIB	148.280	4	37.070	7179.276	0.000	0.997
TECHNIQ	6.396	1	6.396	1238.708	0.000	0.943
PREPROC ×	138.900	8	17.362	3362.559	0.000	0.997
DISTRIB PREPROC ×	9.486	2	4.743	918.554	0.000	0.961
TECHNIQUE DISTRIB ×	1.426	4	0.356	69.036	0.000	0.786
TECHNIQUE PREPROC ×	2.574	8	0.322	62.310	0.000	0.869
DISTRIB × TECHNIQUE Error	0.387	75	0.005
Total	979 340.899	105
		Table XIV. 3-	way ANOVA for t	esting
Source	Type III sum of squares	DF	Mean square	F	Sig.	Partial η²
Model	844 172.942	30	28 139.09	506 683.910	0.000	1.000
PREPROC	1296.821	2	648.411	11 675.541	0.000	0.997
DISTRIB	904.441	4	226.110	4071.431	0.000	0.995
TECHNIQUE	3.537	1	3.537	63.695	0.000	0.459
PREPROC ×	605.016	8	75.627	1361.771	0.000	0.993
DISTRIB PREPROC ×	10.714	2	5.357	96.461	0.000	0.720
TECHNIQUE DISTRIB ×	5.432	4	1.358	24.454	0.000	0.566
TECHNIQUE PREPROC ×	10.002	8	1.250	22.511	0.000	0.706
DISTRIB × TECHNIQUE Error	4.165	75	0.056
Total	844 177.107	105
	Table XV. Pairs’ comparison for ‘preprocessing’ factor

PREPROC^aPREPROC^aMean difference SE Sig. 95% confidence interval

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