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US 12,008,441 B2Granted
Categorical input machine learning models
AssigneeOptum Services (Ireland) Limited
InventorsLorcan B. MacManus, Peter Cogan, Conor Breen
Life of this patent
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Abstract
There is a need for more effective and efficient predictive data analysis based at least in part on categorical input data. This need can be addressed by, for example, solutions for performing predictive data analysis that utilize at least one of categorical level merging, mutual-information-based feature filtering, feature-correlation-based feature filtering to generate training data feature value arrangements, as well as training and using categorical input machine learning models trained using the training data feature value arrangements.
Claims20
A computer-implemented method comprising:
- identifying, by one or more processors, an initial training set comprising a plurality of training data entries associated with a plurality of initial training data features, wherein:
- (i) an initial training data entry of the plurality of training data entries is associated with a respective training data feature value for each of a subset of initial training data features of the plurality of initial training data features and a training target value for a training target feature, and (ii) an initial training data feature of the plurality of initial training data features is associated with a plurality of initial categorical levels;
- generating, by the one or more processors, an adjusted training set based at least in part on the initial training set, wherein generating the adjusted training set comprises:
- generating a plurality of limited noise features based at least in part on the plurality of initial training data features, wherein generating the plurality of limited noise features comprises:
- determining a limited noise subset of the plurality of initial training data features based at least in part on a per-feature mutual information measure for each initial training data feature of the plurality of initial training data features satisfying a condition defined by a mutual information noise floor, and
- generating the adjusted training set based at least in part on the plurality of limited noise features;
- training a categorical input machine learning model based at least in part on the adjusted training set;
- and
- generating, using the trained categorical input machine learning model, one or more inferred predictions based at least in part on a prediction input data entry.
6 more elements
A computing system comprising one or more processors and memory including program code, the program code configured to, with the one or more processors, cause the computing system to:
- identify an initial training set comprising a plurality of training data entries associated with a plurality of initial training data features, wherein:
- (i) an initial training data entry of the plurality of training data entries is associated with a respective training data feature value for each of a subset of initial training data features of the plurality of initial training data features and a training target value for a training target feature, and (ii) each initial training data feature of the plurality of initial training data features is associated with a plurality of initial categorical levels;
- generate an adjusted training set based at least in part on the initial training set, wherein generating the adjusted training set comprises:
- generating a plurality of limited noise features based at least in part on the plurality of initial training data features, wherein generating the plurality of limited noise features comprises:
- determining a limited noise subset of the plurality of initial training data features based at least in part on a per-feature mutual information measure for each initial training data feature of the plurality of initial training data features satisfying a condition defined by a mutual information noise floor, and
- generating the adjusted training set based at least in part on the plurality of limited noise features;
- train a categorical input machine learning model based at least in part on the adjusted training set;
- and
- generate, using the trained categorical input machine learning model, one or more inferred predictions based at least in part on a prediction input data entry.
6 more elements
A computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions configured to:
- identify an initial training set comprising a plurality of training data entries associated with a plurality of initial training data features, wherein:
- (i) an initial training data entry of the plurality of training data entries is associated with a respective training data feature value for each of a subset of initial training data features of the plurality of initial training data features and a training target value for a training target feature, and (ii) each initial training data feature of the plurality of initial training data features is associated with a plurality of initial categorical levels;
- generate, an adjusted training set based at least in part on the initial training set, wherein generating the adjusted training set comprises:
- generating a plurality of limited noise features based at least in part on the plurality of initial training data features, wherein generating the plurality of limited noise features comprises:
- determining a limited noise subset of the plurality of initial training data features based at least in part on a per-feature mutual information measure for each initial training data feature of the plurality of initial training data features satisfying a condition defined by a mutual information noise floor, and
- generating the adjusted training set based at least in part on the plurality of limited noise features;
- train a categorical input machine learning model based at least in part on the adjusted training set;
- and
- generate, using the trained categorical input machine learning model, one or more inferred predictions based at least in part on a prediction input data entry.
6 more elements
Dependencies are inferred from the claim text.