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Table 5 Comparison of Data Mining Algorithms with 7 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 31,424 99.9554 14 0.0445 3.68
Swells 22,506 100 0 0
Interruptions 5440 99.9816 1 0.0184
Unbalances 14,252 99.8878 16 0.1122
No PQ problems 326,348 100 0 0
Overall 399,970 99.9923 31 0.0077
2 Random Tree Sags 31,429 99.9713 9 0.0287 1.75
Swells 22,506 100 0 0
Interruptions 5441 100 0 0
Unbalances 14,258 99.9299 10 0.0701
No PQ problems 326,347 99.9997 1 0.0003
Overall 399,981 99.995 20 0.005
3 Random Forest Sags 31,434 99.9872 4 0.0128 21.25
Swells 22,506 100 0 0
Interruptions 5441 100 0 0
Unbalances 14,262 99.9579 6 0.0421
No PQ problems 326,347 99.9997 1 0.0003
Overall 399,990 99.9973 11 0.0027