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dc.contributor.authorLu, Yafengen_US
dc.contributor.authorGarcia, Rolandoen_US
dc.contributor.authorHansen, Bretten_US
dc.contributor.authorGleicher, Michaelen_US
dc.contributor.authorMaciejewski, Rossen_US
dc.contributor.editorMeyer, Miriah and Takahashi, Shigeo and Vilanova, Annaen_US
dc.date.accessioned2017-06-12T05:21:06Z
dc.date.available2017-06-12T05:21:06Z
dc.date.issued2017
dc.identifier.issn1467-8659
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13210
dc.identifier.urihttp://dx.doi.org/10.1111/cgf.13210
dc.description.abstractPredictive analytics embraces an extensive range of techniques including statistical modeling, machine learning, and data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline. Primary uses have been in data cleaning, exploratory analysis, and diagnostics. For example, scatterplots and bar charts are used to illustrate class distributions and responses. More recently, extensive visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. In this state-of-the-art report, we catalogue recent advances in the visualization community for supporting predictive analytics. First, we define the scope of predictive analytics discussed in this article and describe how visual analytics can support predictive analytics tasks in a predictive visual analytics (PVA) pipeline. We then survey the literature and categorize the research with respect to the proposed PVA pipeline. Systems and techniques are evaluated in terms of their supported interactions, and interactions specific to predictive analytics are discussed. We end this report with a discussion of challenges and opportunities for future research in predictive visual analytics.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.titleThe State-of-the-Art in Predictive Visual Analyticsen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersST1
dc.description.volume36
dc.description.number3
dc.identifier.doi10.1111/cgf.13210
dc.identifier.pages539-562
dc.description.documenttypestar


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