Test time feature ordering with FOCUS

Early, Kirstin; Fienberg, Stephen E.; Mankoff, Jennifer

doi:10.1145/2971648.2971748

Cited by 18 publications

(3 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In ,Hor99]. Inspired by these, we predict cell inheritance with a neural network model and display partial assignments to engage users [EFM16] and reduce manual labor.…”

Section: Machine Learning In Visualizationmentioning

confidence: 99%

LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning

Hong

Trubuil

Isenberg

2022

Computer Graphics Forum

View full text Add to dashboard Cite

We describe LineageD-a hybrid web-based system to predict, visualize, and interactively adjust plant embryo cell lineages. Currently, plant biologists explore the development of an embryo and its hierarchical cell lineage manually, based on a 3D dataset that represents the embryo status at one point in time. This human decision-making process, however, is time-consuming, tedious, and error-prone due to the lack of integrated graphical support for specifying the cell lineage. To fill this gap, we developed a new system to support the biologists in their tasks using an interactive combination of 3D visualization, abstract data visualization, and correctable machine learning to modify the proposed cell lineage. We use existing manually established cell lineages to obtain a neural network model. We then allow biologists to use this model to repeatedly predict assignments of a single cell division stage. After each hierarchy level prediction, we allow them to interactively adjust the machine learning based assignment, which we then integrate into the pool of verified assignments for further predictions. In addition to building the hierarchy this way in a bottom-up fashion, we also offer users to divide the whole embryo and create the hierarchy tree in a top-down fashion for a few steps, improving the ML-based assignments by reducing the potential for wrong predictions. We visualize the continuously updated embryo and its hierarchical development using both 3D spatial and abstract tree representations, together with information about the model's confidence and spatial properties. We conducted case study validations with five expert biologists to explore the utility of our approach and to assess the potential for LineageD to be used in their daily workflow. We found that the visualizations of both 3D representations and abstract representations help with decision making and the hierarchy tree top-down building approach can reduce assignments errors in real practice.

show abstract

“…In ,Hor99]. Inspired by these, we predict cell inheritance with a neural network model and display partial assignments to engage users [EFM16] and reduce manual labor.…”

Section: Machine Learning In Visualizationmentioning

confidence: 99%

LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning

Hong

Trubuil

Isenberg

2022

Computer Graphics Forum

View full text Add to dashboard Cite

show abstract

“…During the critical first ten questions that our approach selects, prediction accuracy improves equally to fixed-order approaches, but prediction certainty is higher. We considered this application as one short example for a framework of prediction-guided feature selection in (Early et al 2016). Here, we include details on the statistical models and methods used to make predictions with partial information and choose a question ordering.…”

Section: Providing Personalized Energy Estimates With the Residential...mentioning

confidence: 99%

Dynamic Question Ordering in Online Surveys

Early

Mankoff

Fienberg

2017

Journal of Official Statistics

Self Cite

View full text Add to dashboard Cite

Online surveys have the potential to support adaptive questions, where later questions depend on earlier responses. Past work has taken a rule-based approach, uniformly across all respondents. We envision a richer interpretation of adaptive questions, which we call Dynamic Question Ordering (DQO), where question order is personalized. Such an approach could increase engagement, and therefore response rate, as well as imputation quality. We present a DQO framework to improve survey completion and imputation. In the general survey-taking setting, we want to maximize survey completion, and so we focus on ordering questions to engage the respondent and collect hopefully all information, or at least the information that most characterizes the respondent, for accurate imputations. In another scenario, our goal is to provide a personalized prediction. Since it is possible to give reasonable predictions with only a subset of questions, we are not concerned with motivating users to answer all questions. Instead, we want to order questions to get information that reduces prediction uncertainty, while not being too burdensome. We illustrate this framework with two case studies, for the prediction and survey-taking settings. We also discuss DQO for national surveys and consider connections between our statistics-based question-ordering approach and cognitive survey methodology.

show abstract

“…However, the number of classifiers can become considerable if there is a large number of features. The fourth category is fully adaptive algorithms, which can choose any feature based on what is most valuable in the current situation [8,9,11,12]. There are only a few algorithms in this category, and they tend to have a high computational cost.…”

Section: Introductionmentioning

confidence: 99%

Cost-Sensitive Feature-Value Acquisition Using Feature Relevance

Kärkkäinen,

Kachuee,

Goldstein

et al. 2019

Preprint

View full text Add to dashboard Cite

In many real-world machine learning problems, feature values are not readily available. To make predictions, some of the missing features have to be acquired, which can incur a cost in money, computational time, or human time, depending on the problem domain. This leads us to the problem of choosing which features to use at the prediction time. The chosen features should increase the prediction accuracy for a low cost, but determining which features will do that is challenging. The choice should take into account the previously acquired feature values as well as the feature costs. This paper proposes a novel approach to address this problem. The proposed approach chooses the most useful features adaptively based on how relevant they are for the prediction task as well as what the corresponding feature costs are. Our approach uses a generic neural network architecture, which is suitable for a wide range of problems. We evaluate our approach on three costsensitive datasets, including Yahoo! Learning to Rank Competition dataset as well as two health datasets. We show that our approach achieves high accuracy with a lower cost than the current state-of-the-art approaches.Preprint. Under review.

show abstract

Test time feature ordering with FOCUS

Cited by 18 publications

References 39 publications

LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning

LineageD: An Interactive Visual System for Plant Cell Lineage Assignments based on Correctable Machine Learning

Dynamic Question Ordering in Online Surveys

Cost-Sensitive Feature-Value Acquisition Using Feature Relevance

Contact Info

Product

Resources

About