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US 12,001,800 B2Granted
Semantic-aware feature engineering
AssigneeFeedzai—Consultadoria e Inovação Tecnológica, S.A.
InventorsPaulo César Gonçalves Marques, Miguel Ramos de Araújo, Bruno Casal Laraña, Nuno Miguel Lourenço Diegues, Pedro Cardoso Lessa e Silva, Pedro Gustavo Santos Rodrigues Bizarro
Life of this patent
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Abstract
In an embodiment, a process for semantic-aware feature engineering includes receiving semantic labels for data fields of training data. Each of the semantic labels is associated with a semantic meaning associated with a corresponding data field. The process includes automatically generating at least one new feature using at least a portion of the semantic labels.
Claims18
A method comprising:
- receiving, by a processor, semantic labels for data fields of training data, wherein each of the semantic labels includes a semantic meaning associated with a corresponding data field;
- training, by the processor, a machine learning model including by automatically generating at least one new feature to improve interpretability of the machine learning model as compared to not using the at least one new feature including by:
- determining, by the processor, that a received semantic label of the received semantic labels meets a transformation condition specified by a transformation, wherein the transformation condition defines attributes of a label to which a corresponding transformation is appliable;
- in response to the determination that the received semantic label meets the transformation condition, applying, by the processor, the transformation to the data fields based at least in part on the semantic labels to determine the at least one new feature including by building a combination of tagged fields by adding the received semantic label and associated data field to a group of tagged fields;
- wherein a tagged field includes a data field and an associated semantic label, and the transformation condition reduces computer processing resources by limiting a transformation scope for the at least one new feature as compared to not considering the transformation condition;
- and
- using, by the processor, the at least one new feature to train the machine learning model;
- providing, by the processor, the trained machine learning model that uses the at least one new feature that improved the interpretability of the machine learning model and consumed the reduced computer processing resources;
- and
- determining, by the processor, a profile characterizing behavior of at least one entity including at least one of users and cards, wherein the profile includes at least one transformation configured to automatically generate the at least one new feature.
7 more elements
A system comprising:
- a processor configured to:
- receive semantic labels for data fields of training data, wherein each of the semantic labels includes a semantic meaning associated with a corresponding data field;
- train a machine learning model including by automatically generating at least one new feature to improve interpretability of the machine learning model as compared to not using the at least one new feature including by:
- determining that a received semantic label meets a transformation condition specified by a transformation, wherein the transformation condition defines attributes of a label to which a corresponding transformation is appliable;
- in response to the determination that the received semantic label meets the transformation condition, applying the transformation to the data fields based at least in part on the semantic labels to determine the at least one new feature including by building a combination of tagged fields by adding the received semantic label and associated data field to a group of tagged fields;
- wherein a tagged field includes a data field and an associated semantic label, and the transformation condition reduces computer processing resources by limiting a transformation scope for the at least one new feature as compared to not considering the transformation condition;
- and
- using the at least one new feature to a machine learning model;
- provide the trained machine learning model that uses the at least one new feature that improved the interpretability of the machine learning model and consumed the reduced computer processing resources;
- and
- determine a profile characterizing behavior of at least one entity including at least one of users and cards, wherein the profile includes at least one transformation configured to automatically generate the at least one new feature.
8 more elements
A computer program product embodied in a non-transitory computer readable storage medium and comprising computer instructions for:
- receiving semantic labels for data fields of training data, wherein each of the semantic labels includes a semantic meaning associated with a corresponding data field;
- training a machine learning model including by automatically generating at least one new feature to improve interpretability of the machine learning model as compared to not using the at least one new feature including by:
- determining that a received semantic label of the received semantic labels meets a transformation condition specified by a transformation, wherein the transformation condition defines attributes of a label to which a corresponding transformation is appliable;
- in response to the determination that the received semantic label meets the transformation condition, applying the transformation to the data fields based at least in part on the semantic labels to determine the at least one new feature including by building a combination of tagged fields by adding the received semantic label and associated data field to a group of tagged fields;
- wherein a tagged field includes a data field and an associated semantic label, and the transformation condition reduces computer processing resources by limiting a transformation scope for the at least one new feature as compared to not considering the transformation condition;
- and
- using the at least one new feature to train the machine learning model;
- providing the trained machine learning model that uses the at least one new feature that improved the interpretability of the machine learning model and consumed the reduced computer processing resources;
- and
- determining a profile characterizing behavior of at least one entity including at least one of users and cards, wherein the profile includes at least one transformation configured to automatically generate the at least one new feature.
7 more elements
Dependencies are inferred from the claim text.