Invention Grant
- Patent Title: Feature engineering in neural networks optimization
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Application No.: US16456076Application Date: 2019-06-28
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Publication No.: US11501137B2Publication Date: 2022-11-15
- Inventor: Craig M. Trim , Mary E. Rudden , Aaron K. Baughman , Stefan A. G. Van Der Stockt , Bernard Freund , Augustina Monica Ragwitz
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agency: Garg Law Firm, PLLC
- Agent Rakesh Garg; Michael O'Keefe
- Main IPC: G06F16/90
- IPC: G06F16/90 ; G06N3/04 ; G06N5/04 ; G06F17/18 ; G06F17/16 ; G06F16/901

Abstract:
A transitive closure data structure is constructed for a pair of features represented in a vector space corresponding to an input dataset. The data structure includes a set of entries corresponding to a set of all possible paths between a first feature in the pair and a second feature in the pair in a graph of the vector space. The data structure is reduced by removing a subset of the set of entries such that only a single entry corresponding to a single path remains in the transitive closure data structure. A feature cross is formed from a cluster of features remaining in a reduced ontology graph resulting from the reducing the transitive closure data structure. A layer is configured in a neural network to represent the feature cross, which causes the neural network to produce a prediction that is within a defined accuracy relative to the dataset.
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