US 2025/0156730 A1Published application

Systems and methods for minimizing dimensionality of a high-dimensionality dataset during feature engineering

AssigneeCapital One Services, LLC

InventorMichael LANGFORD

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Abstract

Systems and methods for minimizing dimensionality of a high-dimensionality dataset during feature engineering. The system achieves this by using a tabular neural network to extract non-linear transformations of features without dramatically increasing the dimensionality of the original dataset. The system receives an original dataset for classification and a defined number of final features (e.g., dimensionality) that result from the synthetic feature creation and the neural network embedding process. Once an architecture of a model is determined, a model is fit on a synthetic feature set (e.g., a second dataset comprising synthetic features) with a given classification as a target.

Claims20

Dependent claims

A system for minimizing development time in artificial intelligence models by automating hyperparameter selection based on dataset fittings of time-series data, the system comprising:

one or more processors;
and
one or more non-transitory, computer-readable mediums comprising instructions that when executed by the one or more processors cause operations comprising:
receiving a first dataset, wherein the first dataset comprises a first feature;
processing the first dataset with a first model to generate a second dataset, wherein the second dataset comprises a second feature, wherein the second feature is a synthetic feature, and wherein the second feature is not included in the first dataset;
receiving a first feature number, wherein the first feature number indicates an embedding dimension for a neural network;
determining a second model based on the first feature number, wherein the second model comprises the neural network;
fitting the second model on the second dataset;
determining a number of neural network embedding features equal to the first feature number;
tuning a first untuned hyperparameter to a specific value to generate a tuned model based on the number of neural network embedding features;
and
generating for display, on a user interface, a recommendation for using the tuned model for time-series forecasting.

9 more elements

One or more non-transitory, computer-readable mediums comprising instructions that when executed by one or more processors cause operations comprising:

receiving a first dataset, wherein the first dataset comprises a first feature;
processing the first dataset with a first model to generate a second dataset, wherein the second dataset comprises a second feature, wherein the second feature is a synthetic feature, and wherein the second feature is not included in the first dataset;
receiving a first feature number, wherein the first feature number indicates an embedding dimension for a neural network;
determining a second model based on the first feature number, wherein the second model comprises the neural network;
and
generating for display, on a user interface, a number of neural network embedding features equal to the first feature number for fitting the second model on the second dataset.

3 more elements

Dependencies are inferred from the claim text.