QUESTO: Interactive Construction of Objective Functions for Classification Tasks
QUESTO: Interactive Construction of Objective Functions for Classification Tasks
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DOI：
10.1111/cgf.13970
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发表时间：
2020-06
影响因子：
2.5
通讯作者：
Subhajit Das;Shenyu Xu;Michael Gleicher;Remco Chang;A. Endert
中科院分区：
文献类型：
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作者：
Subhajit Das;Shenyu Xu;Michael Gleicher;Remco Chang;A. Endert
构建有效的分类器需要向建模算法提供有关训练数据和建模目标的信息，以便创建做出适当权衡的模型。机器学习算法通过设计它们所求解的目标函数，允许灵活地指定这样的元信息。然而，这样的目标函数对于用户来说很难指定，因为它们是他们意图的特定数学公式。在本文中，我们提出了一种方法，允许用户通过交互式可视化界面生成分类问题的目标函数。我们的方法采用了语义交互设计，将可视化中数据元素上的用户交互转换为目标函数项。生成的目标函数由机器学习求解器求解，该机器学习求解器提供可由用户检查的候选模型，并用于建议对规范的改进。我们演示了一个可视化分析系统Questo，供用户操纵目标函数来定义特定于领域的约束。通过用户研究，我们发现Questo可以帮助用户创建满足其目标的各种目标函数。
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.