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Simultaneous Feature Selection and Tuple Selection for Efficient Classification

Simultaneous Feature Selection and Tuple Selection for Efficient Classification

Manoranjan Dash, Vivekanand Gopalkrishnan
Copyright: © 2010 |Pages: 16
ISBN13: 9781605667485|ISBN10: 160566748X|EISBN13: 9781605667492
DOI: 10.4018/978-1-60566-748-5.ch012
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MLA

Dash, Manoranjan, and Vivekanand Gopalkrishnan. "Simultaneous Feature Selection and Tuple Selection for Efficient Classification." Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications, edited by Tho Manh Nguyen, IGI Global, 2010, pp. 270-285. https://doi.org/10.4018/978-1-60566-748-5.ch012

APA

Dash, M. & Gopalkrishnan, V. (2010). Simultaneous Feature Selection and Tuple Selection for Efficient Classification. In T. Nguyen (Ed.), Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications (pp. 270-285). IGI Global. https://doi.org/10.4018/978-1-60566-748-5.ch012

Chicago

Dash, Manoranjan, and Vivekanand Gopalkrishnan. "Simultaneous Feature Selection and Tuple Selection for Efficient Classification." In Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications, edited by Tho Manh Nguyen, 270-285. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-748-5.ch012

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Abstract

Feature selection and tuple selection help the classifier to focus to achieve similar (or even better) accuracy as compared to the classification without feature selection and tuple selection. Although feature selection and tuple selection have been studied earlier in various research areas such as machine learning, data mining, and so on, they have rarely been studied together. The contribution of this chapter is that the authors propose a novel distance measure to select the most representative features and tuples. Their experiments are conducted over some microarray gene expression datasets, UCI machine learning and KDD datasets. Results show that the proposed method outperforms the existing methods quite significantly.

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