Abstract
Manually labeling data sets is a time-consuming and expensive task that can be accelerated by interactive machine learning andvisual analytics approaches. At the core of these approaches are strategies for the selection of candidate instances to label. Weintroduce degree-of-interest (DOI) functions as atomic building blocks to formalize candidate selection strategies. We introducea taxonomy of DOI functions and an approach for the visual analysis of DOI functions, which provide novel complementaryviews on labeling strategies and DOIs, support their in-depth analysis and facilitate their interpretation. Our method shallsupport the generation of novel and better explanation of existing labeling strategies in futu
Users
Please
log in to take part in the discussion (add own reviews or comments).
