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

Claim 21:
22. 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 comprises a neural network trained using group sparse regularization;
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 ML model and the causal graph are generated by co-training a plurality of ML models using a plurality of training feature data sets relating to a plurality of faults and using group regularization to encourage consistency among feature selection for any given node across the plurality of data sets,
wherein the generated causal graph comprises a weighted causal graph, and
wherein the causal graph indicates causal strength for each causal relationship among the plurality of nodes, and
wherein the causal strength is computed based on a norm of input model coefficients corresponding to each node; and
predicting the plurality of future feature values for the node using the ML.