Augmenting Training Data Sets for ML Classifiers Using Classification Metadata

A non-transitory computer readable storage medium having stored thereon program code executable by a computer system, the program code causing the computer system to execute a method comprising:

• training a first machine learning (ML) classifier using a training data set, wherein the training data set comprises a plurality of data instances, wherein each data instance includes a set of features, and wherein the training results in a trained version of the first ML classifier;
• classifying each data instance in the plurality of data instances using the trained version of the first ML classifier, the classifying generating classification metadata for each data instance;
• augmenting the training data set with the classification metadata to create an augmented version of the training data set;
• and
• training a second ML classifier using the augmented version of the training data set.

A computer system comprising:

• a processor;
• and
• a non-transitory computer readable medium having stored thereon program code that, when executed, causes the processor to:
• train a first machine learning (ML) classifier using a training data set, wherein the training data set comprises a plurality of data instances, wherein each data instance includes a set of features, and wherein the training results in a trained version of the first ML classifier;
• classify each data instance in the plurality of data instances using the trained version of the first ML classifier, the classifying generating classification metadata for each data instance;
• augment the training data set with the classification metadata to create an augmented version of the training data set;
• and
• train a second ML classifier using the augmented version of the training data set.