Abstract
For machine learning data reduction and model optimization, a method randomly assigns each data feature of a training data set to a plurality of solution groups. Each solution group has no more than a solution group number k of data features and each data feature is assigned to a plurality of solution groups. The method identifies each solution group as a high-quality solution group or a low-quality solution group. The method further calculates data feature scores for each data feature comprising a high bin number and a low bin number. The method determines level data for each data feature from the data feature scores using a fuzzy inference system. The method identifies an optimized data feature set based on the level data. The method further trains a production model using only the optimized data feature set. The method predicts a result using the production model.
Document Type
Patent
Status
Issued
Issue Date
6-6-2023
Patent Number
US 11669758 [ 11,669,758 ] ; US11669758B2
CPC Classification
G 06 N 7/023
Application number
16/681396
Assignees
Rockwell Automation Technologies, Inc.
Filing Date
11-12-2019
Recommended Citation
Maturana, F. P., & LaCasse, P. M. (2023). Machine Learning Data Feature Reduction and Model Optimization (United States Patent No. US11669758B2). https://scholar.afit.edu/patents/107