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Table 2 Comparison of Data Mining Algorithms with 4 attributes for evaluation on training set

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 31,438 100 0 0 3.59
Swells 22,506 100 0 0
Interruptions 5441 100 0 0
Unbalances 14,257 99.9229 11 0.0771
No PQ problems 326,348 100 0 0
Overall 399,990 99.9973 11 0.0027
2 Random Tree Sags 31,438 100 0 0 1.91
Swells 22,506 100 0 0
Interruptions 5441 100 0 0
Unbalances 14,268 100 0 0
No PQ problems 326,348 100 0 0
Overall 400,001 100 0 0
3 Random Forest Sags 31,438 100 0 0 25.51
Swells 22,506 100 0 0
Interruptions 5441 100 0 0
Unbalances 14,268 100 0 0
No PQ problems 326,348 100 0 0
Overall 400,001 100 0 0