Framework for semi-supervised learning when no labeled data is given

Abstract

A method, including collecting communication sessions, and generating samples from the sessions. Classifiers are applied to the samples, thereby computing a classifier prediction for each sample, and based on the classifier predictions, respective aggregated predictions are determined for the samples. Based on the classifier and the aggregated predictions, a precision and a hit rate for each classifier and a positive rate are computed, and based on the aggregated predictions, a subset of the samples are selected. Using the selected subset, a model including the classifiers is computed based on the precisions, the hit rates and the positive rate, and the model is applied to the samples, thereby updating the classifier and the aggregate predictions. The steps of computing the precision and the hit rate, selecting the subset, computing the model and applying the model are repeated until meeting a halting condition, and using the model, additional communication sessions are scanned.