Surprise Bug Report Prediction Utilizing Optimized Integration with Imbalanced Learning Strategy

Table 7

The prediction results of OIILS and 4 combinations.

Projects	Evaluation	RUS		ROS		CMA		SMOTE		OIILS
		J48	RT	RT	NBM	NB	NBM	KNN	NBM
Ambari	Precision	0.316	0.326	0.275	0.339	0.307	0.293	0.283	0.301	0.311
	Recall	0.453	0.585	0.264	0.415	0.434	0.415	0.672	0.362	0.793
	F-Measure	0.372	0.419	0.269	0.373	0.359	0.344	0.398	0.328	0.447
Camel	Precision	0.429	0.389	0.404	0.45	0.447	0.423	0.389	0.428	0.409
	Recall	0.522	0.457	0.457	0.587	0.37	0.652	0.896	0.604	0.978
	F-Measure	0.471	0.42	0.429	0.509	0.405	0.513	0.542	0.501	0.577
Derby	Precision	0.173	0.165	0.286	0.308	0.143	0.235	0.223	0.358	0.229
	Recall	0.609	0.565	0.261	0.174	0.174	0.174	0.8	0.13	0.826
	F-Measure	0.269	0.255	0.273	0.222	0.157	0.2	0.349	0.191	0.359
Wicket	Precision	0.429	0.333	0.333	0.447	0.365	0.447	0.381	0.472	0.405
	Recall	0.551	0.469	0.327	0.347	0.388	0.429	0.914	0.42	1
	F-Measure	0.482	0.39	0.33	0.391	0.376	0.438	0.537	0.445	0.577
avgPrecision		0.337	0.303	0.324	0.386	0.316	0.35	0.319	0.39	0.339
avgRecall		0.534	0.519	0.327	0.381	0.341	0.418	0.82	0.379	0.899
avgF-Measure		0.399	0.371	0.325	0.374	0.324	0.374	0.456	0.366	0.49