Literature DB >> 27441712

A Framework for Considering Comprehensibility in Modeling.

Michael Gleicher1.   

Abstract

Comprehensibility in modeling is the ability of stakeholders to understand relevant aspects of the modeling process. In this article, we provide a framework to help guide exploration of the space of comprehensibility challenges. We consider facets organized around key questions: Who is comprehending? Why are they trying to comprehend? Where in the process are they trying to comprehend? How can we help them comprehend? How do we measure their comprehension? With each facet we consider the broad range of options. We discuss why taking a broad view of comprehensibility in modeling is useful in identifying challenges and opportunities for solutions.

Keywords:  data analysis; human-computer interaction; machine learning; statistical modeling; visual analytics; visualization

Mesh:

Year:  2016        PMID: 27441712      PMCID: PMC4932655          DOI: 10.1089/big.2016.0007

Source DB:  PubMed          Journal:  Big Data        ISSN: 2167-6461            Impact factor:   2.128


  11 in total

1.  Opening the Black Box: Strategies for Increased User Involvement in Existing Algorithm Implementations.

Authors:  Thomas Mühlbacher; Harald Piringer; Samuel Gratzl; Michael Sedlmair; Marc Streit
Journal:  IEEE Trans Vis Comput Graph       Date:  2014-12       Impact factor: 4.579

2.  An insight-based methodology for evaluating bioinformatics visualizations.

Authors:  Purvi Saraiya; Chris North; Karen Duca
Journal:  IEEE Trans Vis Comput Graph       Date:  2005 Jul-Aug       Impact factor: 4.579

3.  A fast learning algorithm for deep belief nets.

Authors:  Geoffrey E Hinton; Simon Osindero; Yee-Whye Teh
Journal:  Neural Comput       Date:  2006-07       Impact factor: 2.026

4.  Toward measuring visualization insight.

Authors:  Chris North
Journal:  IEEE Comput Graph Appl       Date:  2006 May-Jun       Impact factor: 2.088

5.  A design space of visualization tasks.

Authors:  Hans-Jörg Schulz; Thomas Nocke; Magnus Heitzler; Heidrun Schumann
Journal:  IEEE Trans Vis Comput Graph       Date:  2013-12       Impact factor: 4.579

6.  A multi-level typology of abstract visualization tasks.

Authors:  Matthew Brehmer; Tamara Munzner
Journal:  IEEE Trans Vis Comput Graph       Date:  2013-12       Impact factor: 4.579

7.  Explainers: expert explorations with crafted projections.

Authors:  Michael Gleicher
Journal:  IEEE Trans Vis Comput Graph       Date:  2013-12       Impact factor: 4.579

8.  Unsupervised discovery of nonlinear structure using contrastive backpropagation.

Authors:  Geoffrey Hinton; Simon Osindero; Max Welling; Yee-Whye Teh
Journal:  Cogn Sci       Date:  2006-07-08

9.  Visualizing Validation of Protein Surface Classifiers.

Authors:  A Sarikaya; D Albers; J Mitchell; M Gleicher
Journal:  Comput Graph Forum       Date:  2014-06       Impact factor: 2.078

10.  Comprehensible Predictive Modeling Using Regularized Logistic Regression and Comorbidity Based Features.

Authors:  Gregor Stiglic; Petra Povalej Brzan; Nino Fijacko; Fei Wang; Boris Delibasic; Alexandros Kalousis; Zoran Obradovic
Journal:  PLoS One       Date:  2015-12-08       Impact factor: 3.240

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