International Business Machines Corporation
PERTURBED RECORDS GENERATION

Abstract:

Reducing a count of perturbed records in a machine learning dataset by application of a correlation matrix of feature values identified in training records to reduce the number of features represented in the perturbed records. Deleting one of a pair of correlated records is achieved with reference to a correlation score that identifies features of sufficient similarity to be paired up. Reducing the number of features for which values are assigned in a data perturbation process results in a relatively reduced number of perturbed records.