公开(公告)号：US12248534B2

公开(公告)日：2025-03-11

申请号：US17694288

申请日：2022-03-14

Applicant: SAP SE

Inventor： Mohamed Bouadi ,  Arta Alavi

IPC: G06N3/08 ,  G06F18/21 ,  G06F18/214 ,  G06N3/006 ,  G06N3/084 ,  G06N3/092 ,  G06N5/04 ,  G06N7/01 ,  G06N20/00

Abstract: Systems, methods, and computer-readable media for performing feature engineering on a dataset for predictive modeling are disclosed. A dataset may comprise a plurality of features that are used for the predictive model. The dataset may be fed to a neural network to determine which features have the greatest impact on the predictive model and which features do not positively impact the predictive model. A deep reinforcement learning agent may select an action to perform on the dataset. The action may be applied to the dataset to generate new features and obtain a transformed dataset. Features that do not positively impact the predictive model may be removed from the dataset. A reward may be calculated for the transformed dataset. The transformed dataset and the reward may be passed to the neural network for further iteration and optimization of the features for the predictive model.

公开(公告)号：US12182091B2

公开(公告)日：2024-12-31

申请号：US18168982

申请日：2023-02-14

Applicant: SAP SE

Inventor： Mohamed Bouadi ,  Arta Alavi ,  Salima Benbernou ,  Mourad Ouziri

IPC: G06F16/22 ,  G06N3/092

Abstract: Systems and methods include determination of a plurality of features, determination, for each of the plurality of features, of a feature vector based on a taxonomy of logical entities, combination of the determined feature vectors into a composite feature vector, determination of an operator based on the composite feature vector, and determination of a new feature based on the operator.