Invention Grant
- Patent Title: Method and system for adaptively imputing sparse and missing data for predictive models
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Application No.: US15707500Application Date: 2017-09-18
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Publication No.: US10409789B2Publication Date: 2019-09-10
- Inventor: Michael Zoll , Yaser I. Suleiman , Subhransu Basu , Angelo Pruscino , Wolfgang Lohwasser , Wataru Miyoshi , Thomas Breidt , Thomas Herter , Klaus Thielen , Sahil Kumar
- Applicant: Oracle International Corporation
- Applicant Address: US CA Redwood Shores
- Assignee: ORACLE INTERNATIONAL CORPORATION
- Current Assignee: ORACLE INTERNATIONAL CORPORATION
- Current Assignee Address: US CA Redwood Shores
- Agency: Vista IP Law Group, LLP
- Main IPC: G06F16/30
- IPC: G06F16/30 ; G06F16/215 ; G06K9/62 ; G06F11/34 ; G06N7/00 ; H04L12/24 ; G06N20/20 ; G06N20/00 ; H04L12/26
Abstract:
Described is an approach that provides an adaptive solution to missing data for machine learning systems. A gradient solution is provided that is attentive to imputation needs at each of several missingness levels. This multilevel approach treats data missingness at any of multiple severity levels while utilizing, as much as possible, the actual observed data.
Public/Granted literature
- US20180081914A1 METHOD AND SYSTEM FOR ADAPTIVELY IMPUTING SPARSE AND MISSING DATA FOR PREDICTIVE MODELS Public/Granted day:2018-03-22
Information query
