A novel visual approach for enhanced attribute analysis and selection
Resumo
As a consequence of the current capabilities of collecting and storing data, a data set of many attributes frequently reflects more than one phenomenon. Understanding the role of attribute subsets and their impact on the organization and structure of a data set under study is paramount to many exploratory and analytical tasks. Example applications range from medicine to financial markets, whereby one wishes to locate subsets of variables that impact the prediction of target categorical attributes. The user is essential in this context since automated techniques are not currently capable of embedding user knowledge in attribute selections. In this work, we propose an approach to deal with the analysis and selection of attributes in a data set based on three principles: firstly, we center the analysis of the relationships on categorical attributes or labels, because they usually summarize important state variables in the application; secondly, we express the relationship between target attributes and all others in the data set within a single visualization, providing understanding of a large number of correlations in the same visual frame; thirdly, we propose an interactive dual-visual approach whereby changes and selections in attribute space reflect visually on the configuration of data layouts, conceived to support immediate analysis of the impact of selected subsets of attributes in the organization of the data set. We validate our approach by means of a number of case studies, illustrating distinct scenarios of knowledge acquisition and feature selection.
Palavras-chave:
Visual analytics, Data visualization, Attribute space analysis, Feature selection
Referências
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T. Mühlbacher, H. Piringer. A partition-based framework for building and validating regression models
IEEE Trans Vis Comput Graph, 19 (12) (2013), pp. 1962-1971, 10.1109/TVCG.2013.125
P. Klemm, K. Lawonn, S. Glaßer, U. Niemann, K. Hegenscheid, H. Völzke, et al. 3D regression heat map analysis of population study data. IEEE Transs Vis Comput Graph, 22 (1) (2016), pp. 81-90, 10.1109/TVCG.2015.2468291
J. Bernard, M. Steiger, S. Widmer, H. Lücke-Tieke, T. May, J. Kohlhammer. Visual-interactive exploration of interesting multivariate relations in mixed research data sets. Comput Graph Forum, 33 (3) (2014), pp. 291-300, 10.1111/cgf.12385
T. May, A. Bannach, J. Davey, T. Ruppert, J. Kohlhammer. Guiding feature subset selection with an interactive visualization. Proceedings of the IEEE conference on visual analytics science and technology (VAST) (2011), pp. 111-120, 10.1109/VAST.2011.6102448
Y. Wang, J. Li, F. Nie, H. Theisel, M. Gong, D.J. Lehmann. Linear discriminative star coordinates for exploring class and cluster separation of high dimensional data. Comput Graph Forum, 36 (3) (2017), pp. 401-410, 10.1111/cgf.13197
A. Sanchez, C. Soguero-Ruiz, I. Mora-Jimnez, F. Rivas-Flores, D. Lehmann, M. Rubio-Sánchez. Scaled radial axes for interactive visual feature selection: a case study for analyzing chronic conditions. Expert Syst Appl, 100 (2018), pp. 182-196, 10.1016/j.eswa.2018.01.054
C. Turkay, P. Filzmoser, H. Hauser. Brushing dimensions - A dual visual analysis model for high-dimensional data
IEEE Trans Vis Comput Graph, 17 (12) (2011), pp. 2591-2599, 10.1109/TVCG.2011.178
C. Turkay, A. Lex, M. Streit, H. Pfister, H. Hauser. Characterizing cancer subtypes using dual analysis in Caleydo stratomex. IEEE Comput Graph Appl, 34 (2) (2014), pp. 38-47, 10.1109/MCG.2014.1
X. Yuan, D. Ren, Z. Wang, C. Guo. Dimension projection matrix/tree: interactive subspace visual exploration and analysis of high dimensional data. IEEE Trans Vis Comput Graph, 19 (12) (2013), pp. 2625-2633, 10.1109/TVCG.2013.150
P.E. Rauber, A.X. Falcao, A.C. Telea. Projections as visual aids for classification system design. Inf Vis (2017), 10.1177/1473871617713337
G.M. Draper, Y. Livnat, R.F. Riesenfeld. A survey of radial methods for information visualization. IEEE Trans Vis Comput Graph, 15 (5) (2009), pp. 759-776, 10.1109/TVCG.2009.23
S. Diehl, F. Beck, M. Burch. Uncovering strengths and weaknesses of radial visualizations—An empirical approach
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J. Sharko, G. Grinstein, K.A. Marx. Vectorized Radviz and its application to multiple cluster datasets. IEEE Trans Vis Comput Graph, 14 (6) (2008), pp. 1427-1444, 10.1109/TVCG.2008.173
J.H.P. Ono, F. Sikansi, D.C. Corrł a, F.V. Paulovich, A. Paiva, L.G. Nonato. Concentric Radviz: visual exploration of multi-task classification. Proceedings of the twenty-eighth SIBGRAPI conference on graphics, patterns and images (2015), pp. 165-172, 10.1109/SIBGRAPI.2015.38
F. Zhou, W. Huang, J. Li, Y. Huang, Y. Shi, Y. Zhao. Extending dimensions in Radviz based on mean shift. Proceedings of the IEEE pacific visualization symposium (PacificVis) (2015), pp. 111-115, 10.1109/PACIFICVIS.2015.7156365
S. Cheng, W. Xu, K. Mueller. Radviz deluxe: an attribute-aware display for multivariate data. Processes, 5 (4) (2017), p. 75, 10.3390/pr5040075
L.v.d. Maaten, G. Hinton. Visualizing data using t-sne. J Mach Learn Res, 9 (Nov) (2008), pp. 2579-2605
P. Joia, D. Coimbra, J.A. Cuminato, F.V. Paulovich, L.G. Nonato. Local affine multidimensional projection. IEEE Trans Vis Comput Graph, 17 (12) (2011), pp. 2563-2571, 10.1109/TVCG.2011.220
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Asuncion A., Newman D.. UCI machine learning repository. 2007.
Yi Liu, Y.F. Zheng. One-against-all multi-class SVM classification using reliability measures. Proceedings of the IEEE International Joint Conference on Neural Networks, 2 (2005), pp. 849-854 vol.2, 10.1109/IJCNN.2005.1555963
G. Tsoumakas, I. Katakis. Multi-label classification: an overview. Int J Data Warehous Min, 3 (3) (2006), 10.4018/jdwm.2007070101
B.G. Tabachnick, L.S. Fidell. Using multivariate statistics (5th ed.), 0205459382, Allyn & Bacon, Inc., Needham Heights, MA, USA (2006)
Z. Zhang, K.T. McDonnell, E. Zadok, K. Mueller. Visual correlation analysis of numerical and categorical data on the correlation map. IEEE Trans Vis Comput Graph, 21 (2) (2015), pp. 289-303, 10.1109/TVCG.2014.2350494
J. Mackinlay. Automating the design of graphical presentations of relational information. ACM Trans Graph, 5 (2) (1986), pp. 110-141, 10.1145/22949.22950
S.G. Kobourov. Spring embedders and force directed graph drawing algorithms. CoRR, abs/1201.3011 (2012)
G.A.P. Júnior, S. Scarpelini, A. Basile-Filho, J.I. de Andrade. Índices de trauma Medicina (Ribeirao Preto Online), 32 (3) (1999), pp. 237-250, 10.11606/issn.2176-7262.v32i3p237-250
C. Domingues, R.M.C.d. Sousa, L.d.S. Nogueira, R.S. Poggetti, B. Fontes, D. Muñoz. The role of the new trauma and injury severity score (NTRISS) for survival prediction. Revista da Escola de Enfermagem da USP, 45 (6) (2011), pp. 1353-1358, 10.1590/S0080-62342011000600011
C. Domingues, R. Coimbra, R.S. Poggetti, L. de Souza Nogueira, R.M.C. Sousa. Performance of new adjustments to the TRISS equation model in developed and developing countries. World J Emerg Surg, 12 (1) (2017), p. 17, 10.1186/s13017-017-0129-2
J. Li, J.Z. Wang. Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans Pattern Anal Mach Intell, 25 (9) (2003), pp. 1075-1088, 10.1109/TPAMI.2003.1227984
D. Anguita, A. Ghio, L. Oneto, X. Parra, J.L. Reyes-Ortiz. A public domain dataset for human activity recognition using smartphones. Proceedings of the ESANN, 978-2-87419-081-0 (2013), pp. 437-442
D. Lin, X. Tang. Conditional infomax learning: an integrated framework for feature extraction and fusion
Proceedings of the computer vision – ECCV 2006, 978-3-540-33833-8, Springer Berlin Heidelberg, Berlin, Heidelberg (2006), pp. 68-82, 10.1007/11744023_6
F. Fleuret. Fast binary feature selection with conditional mutual information. J Mach Learn Res, 5 (2004), pp. 1531-1555
A. El Akadi, A. El Ouardighi, D. Aboutajdine. A powerful feature selection approach based on mutual information
Int J Comput Sci Netw Secur, 8 (4) (2008), pp. 116-121
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Publicado
28/10/2019
Como Citar
SILVA, Erasmo Artur; MINGHIM, Rosane.
A novel visual approach for enhanced attribute analysis and selection. In: CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 32. , 2019, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
DOI: https://doi.org/10.5753/sibgrapi.2019.9817.
