Optimizing lossy compression for nonhomogeneous multivariate black-box classification models

A computer system comprising:

• one or more processors;
• and
• one or more hardware storage devices that store instructions that are executable by the one or more processors to cause the computer system to:
• compress a data set in accordance with a data compression rate, resulting in generation of compressed data having a first data quality;
• decompress the compressed data, resulting in generation of decompressed data;
• filter, from the decompressed data, data that is identified as belonging to a selected class of data;
• cause a machine learning (ML) classifier to perform a classification operation on the data that is identified as belonging to the selected class of data;
• determine a classification accuracy of the classification operation performed by the ML classifier;
• determine whether the classification accuracy at least meets a threshold accuracy requirement;
• in response to determining that the classification accuracy at least meets the threshold accuracy requirement, increase the data compression rate, resulting in generation of an increased data compression rate, wherein use of the increased data compression rate causes a subsequent reduction in data quality for data that is compressed at the increased data compression rate as compared to the first data quality;
• and
• in response to determining that the classification accuracy does not at least meet the threshold accuracy requirement, decrease the data compression rate, resulting in generation of a decreased data compression rate, wherein use of the decreased data compression rate causes a subsequent increase in data quality for data that is compressed at the decreased data compression rate as compared to the first data quality.

One or more hardware storage devices that store instructions that are executable by one or more processors to cause the one or more processors to perform operations comprising:

• compressing a data set in accordance with a data compression rate, resulting in generation of compressed data having a first data quality;
• decompressing the compressed data, resulting in generation of decompressed data;
• filtering, from the decompressed data, data that is identified as belonging to a selected class of data;
• causing a machine learning (ML) classifier to perform a classification operation on the data that is identified as belonging to the selected class of data;
• determining a classification accuracy of the classification operation performed by the ML classifier;
• determining whether the classification accuracy at least meets a threshold accuracy requirement;
• in response to determining that the classification accuracy at least meets the threshold accuracy requirement, increasing the data compression rate, resulting in generation of an increased data compression rate, wherein use of the increased data compression rate causes a subsequent reduction in data quality for data that is compressed at the increased data compression rate as compared to the first data quality;
• and
• in response to determining that the classification accuracy does not at least meet the threshold accuracy requirement, decreasing the data compression rate, resulting in generation of a decreased data compression rate, wherein use of the decreased data compression rate causes a subsequent increase in data quality for data that is compressed at the decreased data compression rate as compared to the first data quality.