Machine learning prediction post-processing
OwnerTamas Nagy, Tihamer Kocsisinventors
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Abstract
A plurality of machine learning predictions for consecutive sliding windows over a segment of data are obtained. Each machine learning prediction comprises probabilities for predicted classes in a single sliding window. One or more machine learning predictions fulfilling a volatility condition are removed from the plurality of machine learning predictions in order to get filtered machine learning predictions. Probabilities for each predicted class of the filtered machine learning predictions are added up to a sum probability for each predicted class of the filtered machine learning predictions. The predicted class of the filtered machine learning predictions having a highest sum probability is selected as a dominant class of the segment.
Claims11
A computer-implemented method comprising:
- obtaining a plurality of machine learning predictions for consecutive sliding windows over a segment of data, wherein each machine learning prediction comprises probabilities for predicted classes in a single sliding window;
- removing from the plurality of machine learning predictions one or more machine learning predictions fulfilling a volatility condition in order to get filtered machine learning predictions;
- adding up probabilities for each predicted class of the filtered machine learning predictions to a sum probability for each predicted class of the filtered machine learning predictions;
- and
- selecting the predicted class of the filtered machine learning predictions having a highest sum probability as a dominant class of the segment.
A computing device, comprising:
- a memory;
- and
- a processor coupled to the memory and operable to:
- obtain a plurality of machine learning predictions for consecutive sliding windows over a segment of data, wherein each machine learning prediction comprises probabilities for predicted classes in a single sliding window;
- remove from the plurality of machine learning predictions one or more machine learning predictions fulfilling a volatility condition in order to get filtered machine learning predictions;
- add up probabilities for each predicted class of the filtered machine learning predictions to a sum probability for each predicted class of the filtered machine learning predictions;
- and
- select the predicted class of the filtered machine learning predictions having a highest sum probability as a dominant class of the segment.
A non-transitory computer-readable storage medium that includes executable instructions to cause one or more processors to:
- obtain a plurality of machine learning predictions for consecutive sliding windows over a segment of data, wherein each machine learning prediction comprises probabilities for predicted classes in a single sliding window;
- remove from the plurality of machine learning predictions one or more machine learning predictions fulfilling a volatility condition in order to get filtered machine learning predictions;
- add up probabilities for each predicted class of the filtered machine learning predictions to a sum probability for each predicted class of the filtered machine learning predictions;
- and
- select the predicted class of the filtered machine learning predictions having a highest sum probability as a dominant class of the segment.
Dependencies are inferred from the claim text.