International business machines corporation (20240112066). DATA SELECTION FOR AUTOMATED RETRAINING IN CASE OF DRIFTS IN ACTIVE LEARNING simplified abstract

DATA SELECTION FOR AUTOMATED RETRAINING IN CASE OF DRIFTS IN ACTIVE LEARNING

Organization Name

international business machines corporation

Inventor(s)

Amadou Ba of Navan (IE)

Venkata Sitaramagiridharganesh Ganapavarapu of Elmsford NY (US)

Seshu Tirupathi of Dublin (IE)

Bradley Eck of Dublin (IE)

DATA SELECTION FOR AUTOMATED RETRAINING IN CASE OF DRIFTS IN ACTIVE LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240112066 titled 'DATA SELECTION FOR AUTOMATED RETRAINING IN CASE OF DRIFTS IN ACTIVE LEARNING

Simplified Explanation

The patent application describes a computer-implemented method for retraining a machine learning model in case of a drift. Here is a simplified explanation of the abstract:

• A computer detects a drift in machine learning.
• The computer identifies features and a response of a machine learning model in a database.
• A time window of the drift is determined.
• Data of the features and the response within the time window is extracted from the database.
• The computer determines if the extracted data is sufficient for retraining the machine learning model.
• If the data is not sufficient, the computer interpolates features for a future time horizon.
• The computer also interpolates a response corresponding to the interpolated features.
• The machine learning model is then retrained using the interpolated features and response.

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1. 1. 1. Potential Applications

This technology could be applied in various industries where machine learning models are used, such as finance, healthcare, and marketing.

1. 1. 1. Problems Solved

This technology addresses the issue of model drift in machine learning, ensuring that the model remains accurate and up-to-date over time.

1. 1. 1. Benefits

- Improved accuracy of machine learning models - Automated detection and retraining of models - Enhanced performance in dynamic environments

1. 1. 1. Potential Commercial Applications

"Enhancing Machine Learning Model Stability and Accuracy through Drift Detection and Retraining"

1. 1. 1. Possible Prior Art

One possible prior art could be a similar system that detects drift in machine learning models but does not include the interpolation of features and response for retraining purposes.

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1. 1. 1. 1. Unanswered Questions

1. 1. How does the system handle missing data during the interpolation process?

The abstract does not mention how the system deals with missing data when interpolating features for the future time horizon.

1. 1. What criteria does the system use to determine the sufficiency of extracted data for retraining?

The abstract does not specify the exact criteria or thresholds used by the system to decide if the extracted data is sufficient for retraining the machine learning model.

Original Abstract Submitted

a computer-implemented method, a computer program product, and a computer system for retraining a model in case of a drift in machine learning. a computer detects a drift in machine learning. a computer identifies in a database features and a response of a machine learning model. a computer determines a time window of the drift. a computer extracts, from the database, data of the features and the response in the time window. a computer determines whether extracted data is sufficient for retraining the machine learning model. a computer, in response to determining that the extracted data is not sufficient for retraining the machine learning model, interpolates one or more of the features for a predetermined future time horizon. a computer interpolates a response corresponding to one or more interpolated features. a computer retrains the machine learning model, using the one or more interpolated features and an interpolated response corresponding thereto.