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

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

S. No. Algorithm Cases Tested Correct Classification Incorrect Classification Training Time (s)
No. of Samples Accuracy (%) No. of Samples Inaccuracy (%)
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