Heterogeneous model trees for machine learning with discontinuous datasets
AssigneeRaytheon Company
InventorTrevor Weschler
Life of this patent
Estimating the term…
Abstract
A method includes obtaining at least one dataset containing one or more discontinuities, where the one or more discontinuities split data of the at least one dataset into multiple partitions. The method also includes generating feature crosses associated with the at least one dataset. The method further includes generating a decision tree structure based on at least some of the feature crosses, where (i) the decision tree structure includes multiple leaf nodes and (ii) each leaf node corresponds to a different one of the multiple partitions. In addition, the method includes, for each leaf node of the decision tree structure, generating a machine learning model that models data of the corresponding partition.
Claims20
A method comprising:
- obtaining at least one dataset containing one or more discontinuities, the one or more discontinuities splitting data of the at least one dataset into multiple partitions;
- generating feature crosses associated with the at least one dataset;
- generating a decision tree structure based on at least some of the feature crosses, the decision tree structure comprising multiple leaf nodes, each leaf node corresponding to a different one of the multiple partitions;
- and
- for each leaf node of the decision tree structure, generating a machine learning model that models data of the corresponding partition.
An apparatus comprising:
- at least one processing device configured to:
- obtain at least one dataset containing one or more discontinuities, the one or more discontinuities splitting data of the at least one dataset into multiple partitions;
- generate feature crosses associated with the at least one dataset;
- generate a decision tree structure based on at least some of the feature crosses, the decision tree structure comprising multiple leaf nodes, each leaf node corresponding to a different one of the multiple partitions;
- and
- for each leaf node of the decision tree structure, generate a machine learning model that models data of the corresponding partition.
A non-transitory machine readable medium containing instructions that when executed cause at least one processor to:
- obtain at least one dataset containing one or more discontinuities, the one or more discontinuities splitting data of the at least one dataset into multiple partitions;
- generate feature crosses associated with the at least one dataset;
- generate a decision tree structure based on at least some of the feature crosses, the decision tree structure comprising multiple leaf nodes, each leaf node corresponding to a different one of the multiple partitions;
- and
- for each leaf node of the decision tree structure, generate a machine learning model that models data of the corresponding partition.
Dependencies are inferred from the claim text.