Data mining for classification of power quality problems using WEKA and the effect of attributes on classification accuracy

Protection and Control of Modern Power Systems

Table 3 Comparison of Data Mining Algorithms with 4 attributes for stratified 10-fold cross-validation

S. No.	Algorithm	Cases Tested	Correct Classification		Incorrect Classification		Training Time (s)
S. No.	Algorithm	Cases Tested	No. of Samples	Accuracy (%)	No. of Samples	Inaccuracy (%)	Training Time (s)
1	J48	Sags	31,418	99.9364	20	0.0636	4.58
		Swells	22,506	100	0	0
		Interruptions	5438	99.9448	3	0.0552
		Unbalances	14,242	99.8178	26	0.1822
		No PQ problems	326,340	99.9975	8	0.0025
		Overall	399,944	99.9858	57	0.0142
2	Random Tree	Sags	31,431	99.9777	7	0.0223	1.86
		Swells	22,506	100	0	0
		Interruptions	5441	100	0	0
		Unbalances	14,255	99.9089	13	0.0911
		No PQ problems	326,345	99.9991	3	0.0009
		Overall	399,978	99.9943	23	0.0057
3	Random Forest	Sags	31,429	99.9714	9	0.0286	24.5
		Swells	22,506	100	0	0
		Interruptions	5441	100	0	0
		Unbalances	14,255	99.9089	13	0.0911
		No PQ problems	326,345	99.9991	3	0.0009
		Overall	399,976	99.9938	25	0.0062