Applying Randomness Effectively Based on Random Forests for Classification Task of Datasets of Insufficient Information

Table 5

Comparison of accuracy in other data mining methods.

Number	Method	Accuracy; (%)	Sensitivity	Specificity	AUC; (%)	Remarks
1	FLN tightest fit [3]	78.33	0.6375	0.91	NA	2/3 for training,; 1/3 for testing, once
2	FLN ordered tightest fit [4]	85.56	0.7875	0.91	NA	2/3 for training,; 1/3 for testing, once
3	Concurrent random forests [12]	80.19	NA	NA	89.32	10-fold cross validation
4	SubBag-JRip [5]	78.15	NA	NA	NA	10-fold cross validation
5	BAGGING-JRip [5]	79.26	NA	NA	NA	10-fold cross validation
6	C4.5 [8]	70.19	0.303	0.994	62.6	10-fold cross validation
7	Suggested random forests	85.74	0.7763	0.9167	91.45	10-fold cross validation