Towards Rapid Interactive Machine Learning: Evaluating Tradeoffs of Classification without Representation

Our contribution is the design and evaluation of an interactive machine learning interface that rapidly provides the user with model feedback after every interaction. To address visual scalability, this interface communicates with the user via a ``tip of the iceberg'' approach, where the user interacts with a small set of recommended instances for each class. To address computational scalability, we developed an $O(n)$ classification algorithm that incorporates user feedback incrementally, and without consulting the data's underlying representation matrix. Our computational evaluation showed that this algorithm has similar accuracy to several off-the-shelf classification algorithms with small amounts of labeled data. Empirical evaluation revealed that users performed better using our design compared to an equivalent active learning setup.