Bayesian visual analytics: BaVA

House, Leanna; Leman, Scotland; Han, Chao

doi:10.1002/sam.11253

Cited by 30 publications

(18 citation statements)

References 31 publications

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“…In addition to the differences coming from the learning task, one notable contrast between these works and our method is that their aim is to identify user preferences or opinions, whereas our goal is to use expert knowledge as an additional source of information for an improved prediction model, by integrating it with the knowledge coming from the (small n) data. As a probabilistic approach, our work relates to Cano et al (2011) and House et al (2015), where expert feedback is used for improved learning of Bayesian networks and for visual data exploration, respectively. In Sect.…”

Section: Interactive Learningmentioning

confidence: 99%

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Knowledge elicitation via sequential probabilistic inference for high-dimensional prediction

et al. 2017

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Prediction in a small-sized sample with a large number of covariates, the "small n, large p" problem, is challenging. This setting is encountered in multiple applications, such as in precision medicine, where obtaining additional data can be extremely costly or even impossible, and extensive research effort has recently been dedicated to finding principled solutions for accurate prediction. However, a valuable source of additional information, domain experts, has not yet been efficiently exploited. We formulate knowledge elicitation generally as a probabilistic inference process, where expert knowledge is sequentially queried to improve predictions. In the specific case of sparse linear regression, where we assume the expert has knowledge about the relevance of the covariates, or of values of the regression coefficients, we propose an algorithm and computational approximation for fast and efficient interaction, which sequentially identifies the most informative features on which to query expert knowledge. Evaluations of the proposed method in experiments with simulated and real users show improved prediction accuracy already with a small effort from the expert.

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Section: Interactive Learningmentioning

confidence: 99%

“…Monte Carlo algorithms are used for the computation. House et al (2015) present a framework for interactive visual data exploration. They describe two observation models, principal component analysis and multidimensional scal- ing, that are used for dimensionality reduction to visualise the observations in a two dimensional plot.…”

Section: Examplesmentioning

confidence: 99%

Knowledge elicitation via sequential probabilistic inference for high-dimensional prediction

et al. 2017

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“…In particular, we modified PPCA, MDS, and GTM using BaVA [38] and V2PI [44] approaches, so that users can focus on their spatial analysis of data rather than directly updating statistical parameters of models. We present three examples (one for each modified method) that illustrate the effectiveness of these new models.…”

Section: Resultsmentioning

confidence: 99%

“…The challenge in BaVA is to parameterize the cognitive feedback and update the visualization [38]. First, we determine the dimensions of the data d for which the adjusted observations are similar and different.…”

Section: User Guided Ppcamentioning

confidence: 99%

Semantic Interaction for Visual Analytics: Inferring Analytical Reasoning for Model Steering

Endert

2016

Synthesis Lectures on Visualization

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User interaction in visual analytic systems is critical to enabling visual data exploration.Through interacting with visualizations, users engage in sensemaking, a process of developing and understanding relationships within datasets through foraging and synthesis. For example, two-dimensional layouts of high-dimensional data can be generated by dimension reduction models, and provide users with an overview of the relationships between information. However, exploring such spatializations can require expertise with the internal mechanisms and parameters of these models.The core contribution of this work is semantic interaction, capable of steering such models without requiring expertise in dimension reduction models, but instead leveraging the domain expertise of the user. Semantic interaction infers the analytical reasoning of the user with model updates, steering the dimension reduction model for visual data exploration. As such, it is an approach to user interaction that leverages interactions

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“…For instance, suppose that a user decided Mercer was similar to Wake Forest and Clemson due to their size and playing style. In this case, current work under the Bayesian Visual Analytics (BaVA) paradigm (House, Leman, and Han 2010;Hu et al 2013) provides a principled routine for visualizing teams and taking user inputs to compute similarities between teams in a semi-supervised manner. Specifically, BaVA is a way to weight clustering variables to reflect user preferences.…”

Section: Identifying Neighborsmentioning

confidence: 98%

Nearest-neighbor matchup effects: accounting for team matchups for predicting March Madness

Hoegh

Carzolio

Crandell

et al. 2015

Journal of Quantitative Analysis in Sports

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Recently, the surge of predictive analytics competitions has improved sports predictions by fostering data-driven inference and steering clear of human bias. This article details methods developed for Kaggle's March Machine Learning Mania competition for the 2014 NCAA tournament. A submission to the competition consists of outcome probabilities for each potential matchup. Most predictive models are based entirely on measures of overall team strength, resulting in the unintended "transitive property." These models are therefore unable to capture specific matchup tendencies. We introduce our novel nearest-neighbor matchup effects framework, which presents a flexible way to account for team characteristics above and beyond team strength that may influence game outcomes. In particular we develop a general framework that couples a model predicting a point spread with a clustering procedure that borrows strength from games similar to a current matchup. This results in a model capable of issuing predictions controlling for team strength and that capture specific matchup characteristics.

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Bayesian visual analytics: BaVA

Cited by 30 publications

References 31 publications

Knowledge elicitation via sequential probabilistic inference for high-dimensional prediction

Knowledge elicitation via sequential probabilistic inference for high-dimensional prediction

Semantic Interaction for Visual Analytics: Inferring Analytical Reasoning for Model Steering

Nearest-neighbor matchup effects: accounting for team matchups for predicting March Madness

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