Interpretable feature discovery with grammar-based bayesian optimization

A system comprising:

• at least one programmable processor;
• and
• a non-transitory machine-readable medium storing instructions that, when executed by the at least one programmable processor, cause the at least one programmable processor to perform operations comprising:
• defining, using at least one processor, one or more search parameters and one or more constraints on the one or more search parameters for searching data received from one or more data sources, and searching, using the defined one or more search parameters and one or more constraints, the data received from one or more data sources;
• extracting, using the at least one processor, one or more first features from the searched data, the one or more first features being associated with one or more predictive score values;
• repeating, using the at least one processor, the searching in response to receiving a feedback data responsive to the extracted one or more first features, and generating one or more second features resulting from the repeated searching, and
• evaluating one or more of the first and second features using one or more objective functions, and performing the repeating based on the evaluating,
• the one or more objective functions including at least one of the following:
• a function determining a stand-alone value of a binned first or second feature, a function determining an incremental value of a binned first or second feature, and any combination thereof.

A computer program product comprising a non-transitory machine-readable medium storing instructions that, when executed by at least one programmable processor, cause the at least one programmable processor to perform operations comprising:

• defining, using at least one processor, one or more search parameters and one or more constraints on the one or more search parameters for searching data received from one or more data sources, and searching, using the defined one or more search parameters and one or more constraints, the data received from one or more data sources;
• extracting, using the at least one processor, one or more first features from the searched data, the one or more first features being associated with one or more predictive score values;
• repeating, using the at least one processor, the searching in response to receiving a feedback data responsive to the extracted one or more first features, and generating one or more second features resulting from the repeated searching, and
• evaluating one or more of the first and second features using one or more objective functions, and performing the repeating based on the evaluating,
• the one or more objective functions including at least one of the following:
• a function determining a stand-alone value of a binned first or second feature, a function determining an incremental value of a binned first or second feature, and any combination thereof.