US 11,868,230 B2
Automated unsupervised machine learning utilizing meta-learning
Saket K. Sathe, Armonk, NY (US); Long Vu, Chappaqua, NY (US); Peter Daniel Kirchner, Putnam Valley, NY (US); and Horst Cornelius Samulowitz, Armonk, NY (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Mar. 11, 2022, as Appl. No. 17/692,268.
Prior Publication US 2023/0289277 A1, Sep. 14, 2023
Int. Cl. G06F 11/34 (2006.01); G06N 20/00 (2019.01)
CPC G06F 11/3452 (2013.01) [G06F 11/3428 (2013.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
assessing a performance of a plurality of unsupervised machine learning pipelines against a plurality of data sets;
associating the performance with meta-features corresponding to respective pipeline/data set combinations;
training a supervised meta-learning model using the associated performance and meta-features as training data; and
utilizing the trained model to identify one or more pipelines for processing an input data set.