Systems and methods for detecting low-density training regions of machine-learning classification systems

A system for detecting low-density training regions of machine-learning classification systems, the system comprising:

• a receiving module, stored in memory, that receives:
• a training dataset;
• and
• a sample that is to be classified;
• a training module, stored in memory, that trains, using the training dataset, a classifier;
• an estimating module, stored in memory, that calculates, using the training dataset, a density-estimation model comprising a density estimate of a distribution of the training dataset;
• a determining module, stored in memory, that uses the density-estimation model to determine that the sample falls within a low-density region of the distribution of the training dataset;
• a security module, stored in memory, that:
• determines, based at least in part on the sample falling within the low-density region, that the classifier will not accurately classify the sample;
• and
• performs a security action in response to determining that the classifier will not accurately classify the sample;
• and
• at least one physical processor that executes the receiving module, the training module, the estimating module, the determining module, and the security module.

A non-transitory computer-readable medium comprising one or more computer-executable instructions that, when executed by at least one processor of a computing device, cause the computing device to:

• receive a training dataset;
• train, using the training dataset, a classifier of a machine-learning classification system;
• calculate, using the training dataset, a density-estimation model comprising a density estimate of a distribution of the training dataset;
• associate the density-estimation model with the classifier;
• receive a sample that is to be classified;
• use, before classifying the sample using the classifier, the density-estimation model to determine that the sample falls within a low-density region of the distribution of the training dataset;
• determine, based at least in part on the sample falling within the low-density region, that the classifier will not accurately classify the sample;
• and
• perform a security action in response to determining that the classifier will not accurately classify the sample.