Methods and apparatus to augment classification coverage for low prevalence samples through neighborhood labels proximity vectors

Abstract

Methods, apparatus, systems, and articles of manufacture are disclosed that augment classification for low prevalence samples. An example non-transitory computer readable medium comprises instructions that, when executed, causes a machine to at least classify a data sample using a first classifier, classify the data sample using a second classifier different from the first classifier, the second classifier using a plurality of sensitive hashing (LSH) forests to analyze a sorted plurality of neighbor samples, determine whether a first classification result of the first classifier meets or exceeds a confidence threshold, in response to the first classification result of the first classifier meeting or exceeding the confidence threshold, output the first classification result, and in response to the first classification result of the first classifier not meeting or exceeding the confidence threshold, output a second classification result of the second classifier.