Targeted data acquisition for model training

A computer system comprising:

• a memory;
• and
• a processor in communication with the memory, wherein the computer system is configured to perform a method comprising:
• automatically generating metadata describing samples, of an initial dataset, in neighborhoods of an embedding space in which the samples are embedded, the samples described by the automatically generated metadata being classified by a classification model, and including both correctly classified samples in the neighborhoods and incorrectly classified samples in the neighborhoods;
• identifying attributes of one or more correctly classified samples of the collection of samples and one or more incorrectly classified samples of the collection of samples;
• and
• generating queries based on the identified attributes, the queries tailored, based on the attributes, to retrieve additional training data for training the classification model to more accurately classify samples and avoid incorrect sample classification.

A computer program product comprising:

• a computer readable storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method comprising:
• automatically generating metadata describing samples, of an initial dataset, in neighborhoods of an embedding space in which the samples are embedded, the samples described by the automatically generated metadata being classified by a classification model, and including both correctly classified samples in the neighborhoods and incorrectly classified samples in the neighborhoods;
• identifying attributes of one or more correctly classified samples of the collection of samples and one or more incorrectly classified samples of the collection of samples;
• and
• generating queries based on the identified attributes, the queries tailored, based on the attributes, to retrieve additional training data for training the classification model to more accurately classify samples and avoid incorrect sample classification.