"Smearing" probabilities or how to handle imprecise locations for canonically classification-type problems


I am trying to predict failures at different nodes on a line. Each node has different weather features and hardware/configuration features. For a little under half of the historical failures I have, I know the precise location/exact node where the failure happened, however for the other half I only know a range of nodes the failure could have happened on. My current approach is to "smear" a 100% probability across N nodes that the failure could have happened on. e.g. I know a failure happened between nodes A and D therefore assign A, B, C, and D the value 0.25.

I am currently treating my model as a regression on the probability at each node in keras with a single node output layer but the performance is really poor. For other problems like this, historically I've had plenty of data and more exact locations so I could treat it as a strict binary classification however I've never had to deal with classifications that are "partial" so-to-speak. How would you handle this problem? Is a regression of the probability the best approach? I've had the thought of assigning all possible nodes full 1's instead of fractions and treating it as a standard binary classification but I'm worried that will greatly over represent the unknown location failures.


Posted 2020-04-06T16:44:55.073

Reputation: 11

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