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Performance analysis: an integration of principal component analysis and linear discriminant analysis for a very large number of measured variables

Hamid, Hashibah and Zainon, Fatinah and Tan, Pei Yong (2016) Performance analysis: an integration of principal component analysis and linear discriminant analysis for a very large number of measured variables. Research Journal of Applied Sciences, 11 (11). pp. 1422-1426. ISSN 1815-932X

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Abstract

Principal Components Analysis (PCA) is a variable reduction technique helps to reduce a complex dataset to a lower dimensional subspace. This study is interested to investigate an approach for handling a problem occurred from considering a very large number of measured variables followed by a classification task. For such purpose, PCA has been used to extract and reduce of a very large number of variables that considered in the study. Then, a Linear Discriminant Analysis (LDA) which is commonly used for classification is constructed based on the reduced set of variables. The performance analysis of the constructed PCA+LDA was conducted and compared with the classical LDA Model using different size of sample (n) and different number of independent variables (p). The performance of PCA+LDA and classical LDA Model has been evaluated based on misclassification rate. The results demonstrated that PCA+LDA performed better than the classical LDA Model for small sample case. For large sample size case, PCA+LDA also performed better than the classical LDA especially when the measured independent variables is too large.The overall findings showed that the constructed PCA+LDA can be considered as a good approach for handling a very large number of measured variables and performing classification treatment.

Item Type: Article
Uncontrolled Keywords: LDA, PCA,Classification, misclassification rate, large variables.
Subjects: Q Science > QA Mathematics
Divisions: School of Quantitative Sciences
Depositing User: PM.Dr. Hashibah Hamid
Date Deposited: 06 Apr 2017 06:52
Last Modified: 06 Apr 2017 06:52
URI: https://repo.uum.edu.my/id/eprint/21553

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