BEAMES: Interactive Multimodel Steering, Selection, and Inspection for Regression Tasks
BEAMES: Interactive Multimodel Steering, Selection, and Inspection for Regression Tasks
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BEAMES：回归任务的交互式多模型指导、选择和检查
DOI：
10.1109/mcg.2019.2922592
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发表时间：
2019
影响因子：
1.8
通讯作者：
A. Endert
中科院分区：
文献类型：
--
作者：
Subhajit Das;Dylan Cashman;Remco Chang;A. Endert
交互式模型转向可以帮助人们逐步构建适合其领域和任务的机器学习模型。现有的视觉分析工具允许人们操纵单个模型（例如，由降维模型使用的分配属性权重）。然而，在这种情况下，模型的选择至关重要。如果选择的模型对于任务、数据集或被问到的问题来说是次优的，该怎么办？如果一个不同的模型提供了一个更好的拟合，而不是参数化和操纵这个模型呢？本文提出了一种允许用户检查和引导多个机器学习模型的技术。该技术从更广泛的学习算法和模型类型中引导和采样模型。我们将这种技术纳入一个可视化的分析原型，梁，允许用户通过多模型转向执行回归任务。本文通过一个用例演示了BEAMES的有效性，并讨论了多模型转向的更广泛意义。
Interactive model steering helps people incrementally build machine learning models that are tailored to their domain and task. Existing visual analytic tools allow people to steer a single model (e.g., assignment attribute weights used by a dimension reduction model). However, the choice of model is critical in such situations. What if the model chosen is suboptimal for the task, dataset, or question being asked? What if instead of parameterizing and steering this model, a different model provides a better fit? This paper presents a technique to allow users to inspect and steer multiple machine learning models. The technique steers and samples models from a broader set of learning algorithms and model types. We incorporate this technique into a visual analytic prototype, BEAMES, that allows users to perform regression tasks via multimodel steering. This paper demonstrates the effectiveness of BEAMES via a use case, and discusses broader implications for multimodel steering.