Research Article

Surprise Bug Report Prediction Utilizing Optimized Integration with Imbalanced Learning Strategy

Table 5

The performance of seven classification algorithms.

ProjectsEvaluationNBJ48KNNRTRFNBMSVM

AmbariPrecision0.2730.1780.2680.3890.4120.303Null
Recall0.340.1510.2080.3960.1320.1890
F-Measure0.3030.1630.2340.3930.20.233Null

CamelPrecision0.4570.2710.3570.490.4820.392Null
Recall0.3480.2830.3260.5440.2830.4350
F-Measure0.3950.2770.3410.5160.3560.412Null

DerbyPrecision0.3330.2780.1820.0950.3330.5Null
Recall0.4090.2270.0910.0910.0460.1360
F-Measure0.3670.250.1210.0930.080.214Null

WicketPrecision0.3410.3730.3110.350.2630.5Null
Recall0.3060.3880.2860.4290.1020.2450
F-Measure0.3230.380.2980.3850.1470.329Null

avgPrecision0.3510.2750.280.3310.3720.424Null
avgRecall0.3510.2620.2280.3650.1410.2510
avgF-Measure0.3470.2680.2490.3470.1960.297Null