New Training Data Labeling System for Machine Learning Helps Developers

Machine learning (ML) has become one of the most prominent forms of data analysis for everything from fraud detection to visual quality control. Yet the analytic results can often suffer from insufficiently labeled training data. A team of Georgia Tech researchers has created a system that allows users to more effectively label a training dataset with higher accuracy than current methods. "We are looking at the problem from a data management perspective," said School of Computer Science (SCS) Assistant Professor Xu Chu. "In contrast to a lot of ML research that tries to tackle the lack of sufficient training data from an ML algorithm design perspective, we aim at building a system that helps users effectively label a dataset."