But question based on your use case: why is the dmn taking the management of deciding confidence value in the image analysis’ response?
To clarify, the facematch service is a blackbox which returns a confidence somewhere between 0% and 100% (in addition to age estimate, gender estimate etc).
The business drivers are;
straight through processing as much as possible (maximise efficiency).
No false positives (err on the side of caution).
Hence the DMN decision rules are;
- If confidence >= 95% then accept as a match
- If confidence <= 70% then reject as unmatched
- if 70 < match < 95 then refer.
The challenge I have is these values (70 and 95) are (conservatively) arbitrarily chosen as the characteristics of the black box face match are unknown. Thus the machine learning aspect is over time, learn what the rule parameters should be in order to meet the competing business drivers. In addition, a more sophisticated outcome is just dont tell me what these parameters should be, find me the set of parameters and their corresponding values which gives me the greatest discrimination in order to satisfy the business drivers.
From this perspective, I was thinking more along the lines of principle components analysis or a kohonen network. It could be a random forrest approach etc. For example, the ML may learn that for female images, if the difference between the actual age and the estimated age is large, then set the match threshold to 97%. If the actual age and the estimated age differnce is low, set the acceptance threshold to 88%.
An obvious question now could be, why use a DMN approach then, why not use the AI model to be the decison maker? The advanatge of the DMN table encoding the rules is it then becomes transparent and easily enforced in a commercial contract.
I hope that clarifies,