Interactive visual classification of multivariate data

Ke Bing Zhang; Mehmet A. Orgun; Rajan Shankaran; Du Zhang

doi:10.1109/ICMLA.2012.197

Interactive visual classification of multivariate data

Ke Bing Zhang^*, Mehmet A. Orgun, Rajan Shankaran, Du Zhang

^*Corresponding author for this work

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

3 Citations (Scopus)

Abstract

This study proposes a visual approach for classification of multivariate data based on the enhanced separation feature of a visual technique, called Hypothesis-Oriented Verification and Validation by Visualization (HOV3). In this approach, the user first builds up a visual classifier from a training dataset based on its data projection plotted by HOV3 with a statistical measurement of the training dataset on a 2d space where data points with the same class label are well grouped. Then the user classifies unlabeled data points by projecting them with the labeled data points of the visual classifier together in order to collect the unlabeled data points overlapped by the labeled ones. As a result, this study provides a method which is intuitive and easy to use for data classification by visualization.

Original language	English
Title of host publication	Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Editors	M. Arif Wani, Taghi Khoshgoftaar, Xingquan (Hill) Zhu, Naeem Seliya
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	246-251
Number of pages	6
Volume	2
ISBN (Print)	9780769549132
DOIs	https://doi.org/10.1109/ICMLA.2012.197
Publication status	Published - 2012
Event	11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012 - Boca Raton, FL, United States Duration: 12 Dec 2012 → 15 Dec 2012

Other

Other	11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012
Country/Territory	United States
City	Boca Raton, FL
Period	12/12/12 → 15/12/12

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10.1109/ICMLA.2012.197

Cite this

Zhang, K. B., Orgun, M. A., Shankaran, R., & Zhang, D. (2012). Interactive visual classification of multivariate data. In M. A. Wani, T. Khoshgoftaar, X. Zhu, & N. Seliya (Eds.), Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012 (Vol. 2, pp. 246-251). [6406758] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICMLA.2012.197

Zhang, Ke Bing ; Orgun, Mehmet A. ; Shankaran, Rajan ; Zhang, Du. / Interactive visual classification of multivariate data. Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012. editor / M. Arif Wani ; Taghi Khoshgoftaar ; Xingquan (Hill) Zhu ; Naeem Seliya. Vol. 2 Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2012. pp. 246-251

@inproceedings{83cd444b53694cef83fa463e7fbea87a,

title = "Interactive visual classification of multivariate data",

abstract = "This study proposes a visual approach for classification of multivariate data based on the enhanced separation feature of a visual technique, called Hypothesis-Oriented Verification and Validation by Visualization (HOV3). In this approach, the user first builds up a visual classifier from a training dataset based on its data projection plotted by HOV3 with a statistical measurement of the training dataset on a 2d space where data points with the same class label are well grouped. Then the user classifies unlabeled data points by projecting them with the labeled data points of the visual classifier together in order to collect the unlabeled data points overlapped by the labeled ones. As a result, this study provides a method which is intuitive and easy to use for data classification by visualization.",

author = "Zhang, {Ke Bing} and Orgun, {Mehmet A.} and Rajan Shankaran and Du Zhang",

year = "2012",

doi = "10.1109/ICMLA.2012.197",

language = "English",

isbn = "9780769549132",

volume = "2",

pages = "246--251",

editor = "Wani, {M. Arif} and Taghi Khoshgoftaar and Zhu, {Xingquan (Hill)} and Naeem Seliya",

booktitle = "Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012 ; Conference date: 12-12-2012 Through 15-12-2012",

}

Zhang, KB, Orgun, MA , Shankaran, R & Zhang, D 2012, Interactive visual classification of multivariate data. in MA Wani, T Khoshgoftaar, X Zhu & N Seliya (eds), Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012. vol. 2, 6406758, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 246-251, 11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012, Boca Raton, FL, United States, 12/12/12. https://doi.org/10.1109/ICMLA.2012.197

Interactive visual classification of multivariate data. / Zhang, Ke Bing; Orgun, Mehmet A.; Shankaran, Rajan; Zhang, Du.

Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012. ed. / M. Arif Wani; Taghi Khoshgoftaar; Xingquan (Hill) Zhu; Naeem Seliya. Vol. 2 Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2012. p. 246-251 6406758.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

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AU - Zhang, Ke Bing

AU - Orgun, Mehmet A.

AU - Shankaran, Rajan

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N2 - This study proposes a visual approach for classification of multivariate data based on the enhanced separation feature of a visual technique, called Hypothesis-Oriented Verification and Validation by Visualization (HOV3). In this approach, the user first builds up a visual classifier from a training dataset based on its data projection plotted by HOV3 with a statistical measurement of the training dataset on a 2d space where data points with the same class label are well grouped. Then the user classifies unlabeled data points by projecting them with the labeled data points of the visual classifier together in order to collect the unlabeled data points overlapped by the labeled ones. As a result, this study provides a method which is intuitive and easy to use for data classification by visualization.

AB - This study proposes a visual approach for classification of multivariate data based on the enhanced separation feature of a visual technique, called Hypothesis-Oriented Verification and Validation by Visualization (HOV3). In this approach, the user first builds up a visual classifier from a training dataset based on its data projection plotted by HOV3 with a statistical measurement of the training dataset on a 2d space where data points with the same class label are well grouped. Then the user classifies unlabeled data points by projecting them with the labeled data points of the visual classifier together in order to collect the unlabeled data points overlapped by the labeled ones. As a result, this study provides a method which is intuitive and easy to use for data classification by visualization.

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DO - 10.1109/ICMLA.2012.197

M3 - Conference proceeding contribution

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SN - 9780769549132

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BT - Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012

A2 - Wani, M. Arif

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A2 - Zhu, Xingquan (Hill)

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Zhang KB, Orgun MA , Shankaran R, Zhang D. Interactive visual classification of multivariate data. In Wani MA, Khoshgoftaar T, Zhu X, Seliya N, editors, Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012. Vol. 2. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). 2012. p. 246-251. 6406758 https://doi.org/10.1109/ICMLA.2012.197

Interactive visual classification of multivariate data

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