PROCÉDÉ D'APPRENTISSAGE AUTOMATIQUE PERMETTANT DE GÉNÉRER DES ÉTIQUETTES POUR DES RÉSULTATS FLOUS

摘要

A machine learning method is described for generating labels for members of a training set where the labels are not directly available in the training set data. In a first stage of the method an iterative process is used to gradually build up a list of features ("partition features" herein) which are conceptually related to the class label using a human-in-the loop (expert). In a second part of the process we generate labels for the members of the training set, build up a boosting model using the labeling to come up with additional partition features, score the labeling of the training set members from the boosting model, and then with the human-in-the-loop evaluate a labels assigned to a small subset of the members depending on their score. The labels assigned to some or all of those members in the subset may be flipped depending on the evaluation. The final outcome of the process is an interpretable model that explains how the labels were generated and a labeled set of training data.