. Author manuscript; available in PMC: 2017 Nov 3.

Published in final edited form as: Data Min Knowl Discov. 2016 Feb 15;31(1):1–31. doi: 10.1007/s10618-016-0455-0

Table 2.

Learning the adjusted threshold

Procedure Learn_Threshold (trainData)
Input: Labeled data, trainData;
Output: Adjusted threshold, threshold;

1	[S_iSuccess, S_dSuccess] ←find_Scores (trainData);
2	if (S_iSuccess == ϕ && S_dSuccess ==ϕ)
3	threshold ← 1;
4	else if (S_iSuccess ==ϕ&& S_dSuccess != ϕ)
5	threshold ← max(S_dSuccess) ;
6	else if (S_iSuccess !=ϕ && S_dSuccess == ϕ)
7	threshold ← min(S_ iSuccess) ;
8	else if (S_iSuccess! = ϕ && S_dSuccess != ϕ)
9	threshold ← Decision_Tree (S_iSuccess, S_dSuccess);
10	end if