ADVERSE FEATURES NEUTRALIZATION IN MACHINE LEARNING

ADVERSE FEATURES NEUTRALIZATION IN MACHINE LEARNING

Methods and systems are presented for identifying and neutralizing adverse input features that negatively impact accuracy of a machine learning model. A machine learning model is configured to produce an output based on parameter values corresponding to input features. Each input feature is evaluate...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: ZHOU, Yanzan, JIN, Zhetian
Format: Patent
Sprache:eng ; fre ; ger
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
PHYSICS
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
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Beschreibung
Zusammenfassung:Methods and systems are presented for identifying and neutralizing adverse input features that negatively impact accuracy of a machine learning model. A machine learning model is configured to produce an output based on parameter values corresponding to input features. Each input feature is evaluated with respect to its impact on producing a correct output by the machine learning model. One or more adverse input features that have a negative impact on accuracy of the machine learning model are determined. When a request to assess a data is received, input values associated with the data and corresponding to the set of input features are obtained. One or more input values corresponding to the adverse input features are identified. The one or more input values are altered, and the altered input values along with other unaltered input values are used to generate a more accurate output by the machine learning model.