The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Oct. 24, 2023

Filed:

May. 12, 2021
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Naama Tepper, Koranit, IL;

Esther Goldbraich, Haifa, IL;

Boaz Carmeli, Koranit, IL;

Naama Zwerdling, Haifa, IL;

George Kour, Tel Aviv, IL;

Ateret Anaby Tavor, Givat Ada, IL;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/23 (2019.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06F 16/2365 (2019.01); G06N 20/00 (2019.01);
Abstract

Balancing an imbalanced dataset, by: Receiving a balancing policy and the imbalanced dataset. Performing initial adjustment of the imbalanced dataset to comply with the balancing policy, by: oversampling one or more underrepresented classes, and, if one or more of the classes are overrepresented, undersampling them. Operating a generative machine learning model to generate samples for the one or more underrepresented classes, based on the initially-adjusted dataset. Operating a machine learning classification model to label the generated samples with class labels corresponding to the one or more underrepresented classes. Selecting some of the generated samples which, according to the labeling, have a relatively high probability of preserving their class labels. Composing a balanced dataset which complies with the balancing policy and comprises: the samples belonging to the one or more underrepresented classes, the selected generated samples, and an undersampling of the samples belonging to the one or more overrepresented classes.


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