Robust and Adaptive Artificial Intelligence Modeling

摘要

A particular machine learning architecture involving a two-part artificial intelligence (AI) model is disclosed, with one portion being trained on first data (e.g. older data) and another portion being trained on second data (e.g. newer data) in various embodiments. A robust AI model can be combined with an adaptive AI model to account for long-term trends as well as newly emerging population trends. The model architecture can be constructed using gradient boosting trees, artificial neural networks, or other machine learning models. The adaptive AI model can be re-trained on a more frequent basis than the robust AI model, and can use newer types of data in its classification techniques. The adaptive and robust AI models can be combined using logistic regression to provide unified predictions. Electronic transactions and other types of data subject to potential pattern shifts can thus be more accurately classified.