Binary classification of items of interest in a repeatable process

A system comprising:

• a host machine and a learning machine, each having a respective processor, wherein the processors are in electrical communication with at least one sensor;
• and
• tangible, non-transitory memory on which is recorded instructions for predicting a binary quality status of an item of interest during a repeatable process, wherein the binary quality status includes a passing and a failing binary class, and wherein the learning machine is configured to execute the instructions via the processor of the learning machine to thereby:
• receive signals from the at least one sensor;
• identify a set of candidate features as a function of the received signals;
• extract a plurality of features from the set of candidate features, each being more predictive of the binary quality status relative to other non-extracted features in the set of candidate features;
• map the extracted features to a dimensional space having a number of dimensions that is proportional to the number of extracted features, wherein the dimensional space includes most of the items of interest from the passing binary class and excludes at least 90 percent of the items of interest from the failing binary class;
• and
• record the dimensional space in the memory;
• wherein the host machine is programmed to compare the received signals for a subsequent item of interest to the boundaries of the recorded dimensional space during the repeatable process to thereby predict, in real time, the binary quality status of the subsequent item of interest.