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Abstract Details

Activity Number: 253
Type: Contributed
Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #304661
Title: Modified Linear Discriminant Analysis
Author(s): Jin Hyun Nam*+ and Donguk Kim
Companies: and Sungkyunkwan University
Address: 123-301 Hannam-The Hill APT., Seoul, _, 82, South Korea
Keywords: Modified LDA ; DLDA ; Classification ; Gene expression

Many classification methods has been applied for microarray data to compare the performance. Among these, linear discriminant analysis (LDA) is a favored tool due to its simplicity, robustness and predictive accuracy but when the number of genes is larger than the number of observations, it can not be applied directly because the within-class covariance matrix of the genes is singular. Also, diagonal LDA (DLDA) is simpler model compared to LDA and has better performance in some cases. However, in reality, DLDA requires a strong assumption based on conditional independence. In this paper, we propose the modified LDA (MLDA). MLDA is based on conditional independence but uses the information that has an effect on classification performance with joint probability. We suggest two approaches. One is the case of using gene selection method. The other is no use of gene selection method. We found that MLDA has better performance than LDA or DLDA and is no worse than KNN or SVM in real data analysis.

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