A Framework for Considering Comprehensibility in Modeling

Big Data

Volumn 4, Issue 2, 2016, Pages 75-88

  Gleicher, Michael ^a  

^a University of Wisconsin   (United States)

Author keywords

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

Indexed keywords

COMPREHENSION; COMPUTER INTERFACE; HUMAN; MACHINE LEARNING; THEORETICAL MODEL;

COMPREHENSION; HUMANS; MACHINE LEARNING; MODELS, THEORETICAL; USER-COMPUTER INTERFACE;

EID: 84991769817     PISSN: 21676461     EISSN: 2167647X     Source Type: Journal    
DOI: 10.1089/big.2016.0007     Document Type: Article

References (33)

1. Schulz H-J, Nocke T, Heitzler M, Schumann H. A design space of visualization tasks. IEEE Trans Vis Comput Graphics. 2013;19:2366-2375.
2. Huysmans J, Baesens B, Vanthienen J. Using rule extraction to improve the comprehensibility of predictive models. SSRN 2006. Available at: http://dx.doi.org/10.2139/ssrn.961358
3. Stiglic G, Povalej Brzan P, Fijacko N, Wang F, Delibasic B, Kalousis A, Obradovic Z. Comprehensible predictive modeling using regularized logistic regression and comorbidity based features. PLoS One. 2015;10:e0144439.
4. Zeiler M, Fergus R. Visualizing and understanding convolutional networks. In Fleet D, Pajdla T, Schiele B, Tuytelaars T (Eds.): ECCV 2014, Volume 8689 of Lecture Notes in Computer Science, Cham: Springer International Publishing, 2014. pp. 818-833.
5. Munzner T. Visualization Analysis and Design. Boca Raton, FL, CRC Press, 2014.
6. Brehmer M, Munzner T. A multi-level typology of abstract visualization tasks. IEEE Trans Vis Comput Graphics. 2013;19:2376-2385.
7. Craven M. Extracting comprehensible models from trained neural Networks. Ph.D. Dissertation, University of Wisconsin-Madison, 1996.
8. Bertolucci J. Big data analytics: Descriptive vs. predictive vs. prescriptive. Information Week, December 2013.
9. NIST/SEMATECH. e-Handbook of statistical methods. Technical Report, 2012.
10. Anhalt CO, Cortez R. Mathematical modeling: A structured process. Math Teacher. 2015;108:446-452.
11. Marban O, Mariscal G, Segovia J. A data mining & knowledge discovery process model. In: Ponce J, Karahoc A (Eds.). Data Mining and Knowledge Discovery in Real Life Applications, Chapter 1. I-Tech Education and Publishing, 2009.
12. Kurgan LA, Musilek P. A survey of knowledge discovery and data mining process models. Knowledge Eng Rev. 2006;21:1-24.
13. Gleicher M. Position paper: Towards comprehensible predictive modeling. In: Visualization for Predictive Analytics Workshop, Paris, France, 2014.
14. Gleicher M. Explainers: Expert explorations with crafted projections. IEEE Trans Vis Comput Graphics. 2013;19:2042-2051.
15. Ertekin S, Rudin C. A Bayesian approach to learning scoring systems. Big Data. 2015;3:267-276.
16. Johansson U, Konig R, Niklasson L. Automatically balancing accuracy and comprehensibility in predictive modeling. In: 2005 7th International Conference on Information Fusion, volume 2, p. 7, 2005.
17. Erhan D, Bengio Y, Courville A, Vincent P. Visualizing higher-layer features of a deep network. Technical Report, Department IRO, University of Montreal, 2009.
18. Mohamed A-R, Hinton G, Penn G. Understanding how deep belief networks perform acoustic modelling. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, 2012. pp. 4273-4276.
19. Hinton G, Osindero S, Welling M, Teh Y-W. Unsupervised discovery of nonlinear structure using contrastive backpropagation. Cogn Scie. 2006;30:725-731.
20. Tzeng F-Y, Ma K-L. Opening the black box-Data driven visualization of neural networks. In: VIS 05. IEEE Visualization. Minneapolis, MN, 2005. pp. 383-390.
21. Cortez P, Embrechts MJ. Opening black box data mining models using sensitivity analysis. In: 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Paris, France, 2011. pp. 341-348.
22. Hinton GE, Osindero S, Teh Y-W. A fast learning algorithm for deep belief nets. Neural Comput. 2006;18:1527-1554.
23. Lee H, Grosse R, Ranganath R, Ng AY. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th Annual International Conference on Machine Learning-ICML'09, New York, NY, 2009. pp. 1-8.
24. Golino HF, Gomes CMA. Visualizing random forest prediction results. Psychology. 2014;5:2084-2098.
25. Chuang J, Socher R. Interactive visualizations for deep learning. In: Visualization for Predictive Analytics Workshop, Paris, France, 2014.
26. Alexander E, Kohlmann J, Valenza R, Witmore M, Gleicher M. Serendip: Topic model-driven visual exploration of text corpora. In: 2014 IEEE Conference on Visual Analytics Science and Technology (VAST), Paris, France, 2014. pp. 173-182.
27. Muhlbacher T, Piringer H, Gratzl S, Sedlmair M, Streit M. Opening the black box: Strategies for increased user involvement in existing algorithm implementations. IEEE Trans Vis Comput Graphics. 2014;20:1643-1652.
28. Moody D. Cognitive load effects on end user understanding of conceptual models: An experimental analysis. In Benczúr A, Demetrovics J, Gottlob G (Eds.): Advances in Databases and Information Systems: LNCS 3255, volume 3255 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, Berlin, Heidelberg, 2004. pp. 129-143.
29. Zugal S, Pinggera J, Reijers H, Reichert M, Weber B. Making the case for measuring mental effort. In: Proceedings of the Second Edition of the International Workshop on Experiences and Empirical Studies in Software Modelling-EESSMod'12, New York, NY, 2012. p. 1.
30. North C. Toward measuring visualization insight. IEEE Comput Graph Appl. 2006;26:6-9.
31. Saraiya P, North C, Duca K. An insight-based methodology for evaluating bioinformatics visualizations. IEEE Trans Vis Comput Graphics. 2005;11:443-456.
32. Sarikaya A, Albers D, Mitchell J, Gleicher M. Visualizing validation of protein surface classifiers. Comput Graphics Forum. 2014;33:171-180.
33. Gotz D, Sun J. Visualizing accuracy to improve predictive model performance. In: Visualization for Predictive Analytics Workshop, Paris, France, 2014.