CPC G06F 21/55 (2013.01) [G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 20/20 (2019.01)] | 20 Claims |
1. A method for predicting results using ensemble models, the method comprising:
receiving a first trained model data set from a first model source node, the first trained model data set comprising:
a first trained model,
a first critical feature list comprising critical properties of the first trained model data set, wherein the critical properties of the first training model data set are inputs for the first trained model relevant to a result produced by the first trained model, and
a first missing feature generator configured to generate missing critical properties of the first trained model data set based on the first critical feature list;
receiving a second trained model data set from a second model source node, the second trained model data set comprising:
a second trained model,
a second critical feature list comprising critical properties of the second trained model data set, wherein the critical properties of the second trained model data set are inputs for the second trained model relevant to a result produced by the second trained model, and
a second missing feature generator, configured to generate missing critical properties of the second trained model data set based on the second critical feature list;
receiving a prediction request data set comprising a third critical feature list, the third critical feature list comprising critical properties of the prediction request data set, wherein the critical properties of the prediction request data set are properties to be compared to the first critical feature list and the second critical feature list;
making a first determination that the third critical feature list is missing at least one critical feature from the first critical feature list;
generating, based on the first determination and using the first missing feature generator and using a General Adversarial Network (GAN), a first substitute feature to replace the at least one missing critical feature, to obtain a modified prediction request data set;
executing the first trained model using the modified prediction request data set to obtain a first prediction;
executing the second trained model using the prediction request data set to obtain a second prediction; and
obtaining a final prediction using the first prediction and the second prediction as inputs to an ensemble model.
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