Building effective classifiers requires providing the modeling algorithms with information about the training data and modeling goals in order to create a model that makes proper tradeoffs. Machine learning algorithms allow for flexible specification of such meta-information through the design of the objective functions that they solve. However, such objective functions are hard for users to specify as they are a specific mathematical formulation of their intents. In this paper, we present an approach that allows users to generate objective functions for classification problems through an interactive visual interface. Our approach adopts a semantic interaction design in that user interactions over data elements in the visualization are translated into objective function terms. The generated objective functions are solved by a machine learning solver that provides candidate models, which can be inspected by the user, and used to suggest refinements to the specifications. We demonstrate a visual analytics system QUESTO for users to manipulate objective functions to define domain-specific constraints. Through a user study we show that QUESTO helps users create various objective functions that satisfy their goals.

@Article{DXGCE20,
  author       = "Das, Subhajit and Xu, Shenyu and Gleicher, Michael and Chang, Remco and Endert, Alex",
  title        = "QUESTO: Interactive Construction of Objective Functions for Classification Tasks",
  journal      = "Computer Graphics Forum",
  number       = "3",
  volume       = "39",
  month        = "May",
  year         = "2020",
  ee           = "https://diglib.eg.org/handle/10.1111/cgf13970",
  doi          = "10.1111/cgf.13970 ",
  url          = "http://graphics.cs.wisc.edu/Papers/2020/DXGCE20"
}