Interactive visual classification of multivariate data

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

2 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 languageEnglish
Title of host publicationProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
EditorsM. Arif Wani, Taghi Khoshgoftaar, Xingquan (Hill) Zhu, Naeem Seliya
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages246-251
Number of pages6
Volume2
ISBN (Print)9780769549132
DOIs
Publication statusPublished - 2012
Event11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012 - Boca Raton, FL, United States
Duration: 12 Dec 201215 Dec 2012

Other

Other11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012
CountryUnited States
CityBoca Raton, FL
Period12/12/1215/12/12

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