Discovering hidden knowledge in data classification via multivariate analysis

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)90-100
Journal / PublicationExpert Systems
Volume27
Issue number2
Publication statusPublished - May 2010

Abstract

A new classification algorithm based on multivariate analysis is proposed to discover and simulate the grading policy on school transcript data sets. The framework comprises three major steps. First, factor analysis is adopted to separate the scores of several different subjects into grading-related ones and grading-unrelated ones. Second, multidimensional scaling is employed for dimensionality reduction to facilitate subsequent data visualization and interpretation. Finally, a support vector machine is trained to classify the filtered data into different grades. This work provides an attractive framework for intelligent data analysis and decision making. It also exhibits the advantages of high classification accuracy and supports intuitive data interpretation. © 2009 Blackwell Publishing Ltd.

Research Area(s)

  • Data visualization, Dimensionality reduction, Factor analysis, Feature selection, Multidimensional scaling, Multivariate analysis, Pattern classification, Support vector machine

Citation Format(s)

Discovering hidden knowledge in data classification via multivariate analysis. / Chen, Yisong; Ip, Horace H.S.; Li, Sheng; Wang, Guoping.

In: Expert Systems, Vol. 27, No. 2, 05.2010, p. 90-100.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review