Improved nearest centroid classifier with shrunken distance measure for null LDA method on cancer classification problem

Sharma, Alokanand and Paliwal, K.K. (2010) Improved nearest centroid classifier with shrunken distance measure for null LDA method on cancer classification problem. Electronics Letters, 46 (18). pp. 1251-1252. ISSN 0013-5194

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Abstract

Null linear discriminant analysis (LDA) is a well-known dimensionality reduction technique for the small sample size problem. When the null LDA technique projects the samples to a lower dimensional space, the covariance matrices of individual classes become zero, i.e. all the projected vectors of a given class merge into a single vector. In this case, only the nearest centroid classifier (NCC) can be applied for classification. To improve the classification performance of NCC in the reduced-dimensional space, a shrunken distance based NCC technique is proposed that uses class-conditional a priori probabilities for distance computation. Experiments on several DNA microarray gene expression datasets using the proposed technique show very encouraging results for cancer classification.

Item Type:	Journal Article
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
Depositing User:	Ms Mereoni Camailakeba
Date Deposited:	29 Nov 2010 19:41
Last Modified:	18 Jul 2012 02:09
URI:	https://repository.usp.ac.fj/id/eprint/1918

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