Cleaning a machine learning dataset using coresets

A computerized method of cleaning a data set, the method performed by a processing circuitry of a data cleaning system, the method comprising:

• (a) provide the data set, the data set being configured for training of a machine learning model,
• the data set comprising a plurality of data instances, each data instance of the plurality of data instances comprising a label;
• (b) computing at least one coreset from the data set,
• for generating an importance measure for each coreset sample of the at least one coreset, the importance measure being indicative of at least one of an importance value and a weight value;
• (c) selecting, from the coresets, at least one selected coreset that is representative of the data set,
• the at least one selected coreset constituting a representative sub-set of the coresets;
• and
• (d) selecting, from the representative sub-set, a number of coreset samples, based on at least one importance-related criterion,
• the at least one importance-related criterion being indicative of corresponding importance measures of the at least one selected coreset having at least one of high importance values and low weight values,
• giving rise to a set of selected coreset samples having a high probability of at least one of high importance values and low weight values, as compared to a second probability associated with selecting a corresponding number of data instances from the data set,
• the set of selected coreset samples facilitating determining, for each coreset sample of the set of selected coreset samples whether the each coreset sample requires an action,
• thereby facilitating performing the action, which brings about an increased-quality representative sub-set of coresets, as compared to a quality of the representative sub-set,
• the increased-quality representative sub-set of coresets facilitating at least one of a quicker training of the machine learning model and a higher-accuracy training of the machine learning model, as compared to a second training of the machine learning model performed utilizing the data set.