A visual approach for classification based on data projection

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

1 Citation (Scopus)

Abstract

In this paper we present a visual approach for classification in data mining, based on the enhanced separation feature of a visual technique, called Hypothesis-Oriented Verification and Validation by Visualization (HOV 3). In this approach, the user first projects a labeled dataset by HOV 3with a statistical measurement of the dataset on a 2d space, where data points with the same class label are well separated into groups. Then each well separated group and its measure vector are employed as a visual classifier to classify unlabeled data points by projecting and grouping them together with the overlapping labeled data points. The experiments demonstrate that our approach is effective to assist the user on classification of data by visualization.

Original languageEnglish
Title of host publicationPRICAI 2012
Subtitle of host publicationtrends in artificial intelligence: 12th Pacific Rim International Conference on Artificial Intelligence, Kuching, Malaysia, September 3-7 2012: proceedings
EditorsPatricia Anthony, Mitsuru Ishizuka, Dickson Lukose
Place of PublicationHeidelberg, Germany
PublisherSpringer, Springer Nature
Pages850-856
Number of pages7
ISBN (Print)9783642326943
DOIs
Publication statusPublished - 2012
Event12th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2012 - Kuching, Malaysia
Duration: 3 Sep 20127 Sep 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7458 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other12th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2012
CountryMalaysia
CityKuching
Period3/09/127/09/12

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