A Visual Approach for Interactive Co Training
Co training is a popular semi supervised method to build classifiers by combining labeled and unlabeled data. It trains two classifiers with a small amount of initially labeled data and iteratively retrains them after exchanging their high confidence instances. As the initial amount of labels is very small, however, the performance can suffer from the label pollution problem. We therefore propose an interactive visual approach that improves the stability of co training through user inspection of transferred instances. It includes a visualization of classifier uncertainties and disagreement. It further helps users to quickly identify possible mistakes of the automatic approach by guiding user's attention to the instances which are labeled differently than the majority of their nearest neighbors and instances which are labeled differently by the two base classifiers. To help users examine such instances, we also include a visual explanation which shows important features of an instance along with its raw data. We show the effectiveness of our approach with a usage scenario and by comparing it with the classical cotraining approach through experiments. Finally, we discuss limitations and propose several possibilities for future improvement.