Surprise Bug Report Prediction Utilizing Optimized Integration with Imbalanced Learning Strategy

Table 6

The performance of 10 groups of classification algorithms.

Projects	Evaluation	G1	G2	G3	G4	G5	G6	G7	G8	G9	G10
Ambari	Precision	0.308	0.378	0.382	0.305	0.28	0.329	0.313	0.311	0.425	0.301
	Recall	0.83	0.698	0.491	0.868	0.698	0.509	0.887	0.793	0.585	0.83
	F-Measure	0.449	0.49	0.43	0.451	0.4	0.4	0.463	0.447	0.492	0.442
Camel	Precision	0.414	0.433	0.385	0.414	0.414	0.424	0.414	0.409	0.439	0.414
	Recall	1	0.63	0.326	1	1	0.609	1	0.978	0.63	1
	F-Measure	0.586	0.513	0.353	0.586	0.586	0.5	0.586	0.577	0.518	0.586
Derby	Precision	0.211	0.143	0.143	0.208	0.218	0.182	0.215	0.229	0.206	0.222
	Recall	0.826	0.304	0.217	0.87	0.826	0.348	0.87	0.826	0.304	0.87
	F-Measure	0.336	0.194	0.172	0.336	0.346	0.239	0.345	0.359	0.246	0.354
Wicket	Precision	0.398	0.392	0.37	0.378	0.397	0.397	0.383	0.405	0.385	0.381
	Recall	1	0.633	0.347	0.98	0.98	0.592	1	1	0.51	0.98
	F-Measure	0.57	0.484	0.358	0.546	0.565	0.475	0.554	0.577	0.439	0.549
avgPrecision		0.333	0.336	0.32	0.326	0.328	0.333	0.331	0.339	0.364	0.33
avgRecall		0.914	0.566	0.345	0.929	0.876	0.514	0.939	0.899	0.508	0.92
avgF-Measure		0.485	0.421	0.328	0.48	0.474	0.404	0.487	0.49	0.424	0.483