Automatic feature learning from a relational database for predictive modelling
AssigneeInternational Business Machines Corporation
InventorsBeat Buesser, Thanh Lam Hoang, Mathieu Sinn, Ngoc Minh Tran
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Abstract
Embodiments for automatic feature learning for predictive modelling in a computing environment by a processor. A first table and a second table are joined based on an edge between the first table and the second table defined by an entity graph thereby creating a resulting joined table that is connected by a column of data. The resulting joined table is used as an input into one or more neural network operations that transform the resulting joined table to one or more features to predict a target variable.
Claims14
A method, by a processor, for automatic feature learning for predictive modelling in a computing environment, comprising:
- joining a first table and a second table based on an edge between the first table and the second table defined by an entity graph, wherein a resulting joined table is connected by a column of data;
- using the resulting joined table as an input into one or more neural network operations that transform the resulting joined table to one or more features to predict a target variable;
- defining the one or more neural network operations as one or more relational neural network operations and an embedded neural network operation in an embedded layer associated with the resulting joined table;
- and
- inputting numerical data according to a hierarchical order of the resulting joined table into the one or more relational neural network operations that transform the numerical data to one or more features to predict the target variable.
A system for automatic feature learning for predictive modelling in a computing environment, comprising:
- one or more computers having a physical processor and memory, the memory storing executable instructions that when executed cause the system to:
- join a first table and a second table based on an edge between the first table and the second table defined by an entity graph, wherein a resulting joined table is connected by a column of data;
- use the resulting joined table as an input into one or more neural network operations that transform the resulting joined table to one or more features to predict a target variable;
- define the one or more neural network operations as one or more relational neural network operations and an embedded neural network operation in an embedded layer associated with the resulting joined table;
- and
- input numerical data according to a hierarchical order of the resulting joined table into the one or more relational neural network operations that transform the numerical data to one or more features to predict the target variable.
A computer program product for, by a processor, automatic feature learning for predictive modelling in a computing environment, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:
- an executable portion that joins a first table and a second table based on an edge between the first table and the second table defined by an entity graph, wherein a resulting joined table is connected by a column of data;
- an executable portion that uses the resulting joined table as an input into one or more neural network operations that transform the resulting joined table to one or more features to predict a target variable;
- an executable portion that defines the one or more neural network operations as one or more relational neural network operations and an embedded neural network operation in an embedded layer associated with the resulting joined table;
- and
- an executable portion that inputs numerical data according to a hierarchical order of the resulting joined table into the one or more relational neural network operations that transform the numerical data to one or more features to predict the target variable.
Dependencies are inferred from the claim text.