Method of extracting unsuitable and defective data from plurality of pieces of training data used for learning of machine learning model, information processing device, and non-transitory computer-readable storage medium storing computer program

Abstract

A method according to the present disclosure includes selecting a reference class from a plurality of classes, calculating a plurality of degrees of similarity between a feature spectrum corresponding to target training data and a plurality of the feature spectra belonging to the reference class, applying, to the plurality of degrees of similarity, a defectiveness function that is determined in advance, and calculating a defectiveness index with respect to the target training data, and determining whether the target training data is the defective data, based on a result of comparison between the defectiveness index and a threshold value.