Method for transforming data elements within a classification system based in part on input from a human annotator or expert

摘要

A method is provided for transforming data elements within a classification system based in part on input from a human annotator or expert. A first concept evolution model as a training set is composed from a first set of selectively determinable annotations and the first concept evolution model. A trained model is generated after training a learning algorithm with the training set and the concept evolution model. A confidence factor is computed that a predicted annotation is accurately identified. A selected element instance and a corresponding suggested annotation are identified to have a low confidence factor. The classifying of the applied annotation is adjusted where a second concept evolution model is composed for more accurate classifying of the data item.