Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.chemolab.2006.08.007
Title: Partial correlation based variable selection approach for multivariate data classification methods
Authors: Raghuraj Rao, K. 
Lakshminarayanan, S. 
Keywords: Data classification
Discriminant Analysis
Genetic algorithm
Multivariate statistics
Partial correlation coefficients
Variable importance measure
Variable selection
Issue Date: 15-Mar-2007
Source: Raghuraj Rao, K., Lakshminarayanan, S. (2007-03-15). Partial correlation based variable selection approach for multivariate data classification methods. Chemometrics and Intelligent Laboratory Systems 86 (1) : 68-81. ScholarBank@NUS Repository. https://doi.org/10.1016/j.chemolab.2006.08.007
Abstract: Selection of meaningful features characterizing the given set of system observations into distinct classes is crucial in all classification problems. A new significant attribute selection method based on partial correlation coefficient matrix (PCCM) is proposed. Many well studied representative classification data sets with different sizes and types are selected for investigating the performance. Linear Discriminant Analysis (LDA) combined with dimensional reduction techniques is employed as benchmark classifier to validate the new approach. The correlated attributes are arranged in order of their significance to multi-group data classification performance before applying the classification algorithm. Varying number of attributes are retained for the final analysis after PCCM based selection and progressive prediction accuracies are used to compare existing algorithms with the proposed feature selection algorithm. LDA results after PCCM based attribute selection show improvement in prediction efficiencies. It is shown that the PCCM based method is a better variable selection method compared to existing methods for obtaining the optimum set of predictor variables. © 2006 Elsevier B.V. All rights reserved.
Source Title: Chemometrics and Intelligent Laboratory Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/64373
ISSN: 01697439
DOI: 10.1016/j.chemolab.2006.08.007
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