Patent ID: 11860720
Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A method comprising:
receiving historical feature data relating to a plurality of nodes in a system;
generating a machine learning (ML) model for a node of the plurality of nodes,
wherein the ML model is trained to predict a plurality of future feature values for the node based on at least a portion of the historical feature data, and
wherein the ML model is a non-linear neural network; and

generating a causal graph for the plurality of nodes using a feature selection mechanism within the ML model, wherein the feature selection mechanism comprises a regularization term encouraging sparsity of nodes in selected features in the ML model,
wherein the regularization term comprises group-sparse regularization in which all features of each node of the plurality of nodes are forced to be either contributing or not-contributing.