Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.patcog.2008.07.005
Title: Variable predictive models-A new multivariate classification approach for pattern recognition applications
Authors: Raghuraj, R. 
Lakshminarayanan, S. 
Keywords: Data classification
Discriminant analysis
Machine learning
Multivariate statistics
Variable predictive models
Issue Date: Jan-2009
Source: Raghuraj, R., Lakshminarayanan, S. (2009-01). Variable predictive models-A new multivariate classification approach for pattern recognition applications. Pattern Recognition 42 (1) : 7-16. ScholarBank@NUS Repository. https://doi.org/10.1016/j.patcog.2008.07.005
Abstract: Many pattern recognition algorithms applied in literature exhibit data specific performances and are also computationally intense and complex. The data classification problem poses further challenges when different classes cannot be distinguished just based on decision boundaries or conditional discriminating rules. As an alternate to existing methods, inter-relations among the feature vectors can be exploited for distinguishing samples into specific classes. Based on this idea, variable predictive model based class discrimination (VPMCD) method is proposed as a new and alternative classification approach. Analysis is carried out using seven well studied data sets and the performance of VPMCD is benchmarked against well established linear and non-linear classifiers like LDA, kNN, Bayesian networks, CART, ANN and SVM. It is demonstrated that VPMCD is an efficient supervised learning algorithm showing consistent and good performance over these data sets. The new VPMCD method has the potential to be effectively and successfully extended to many pattern recognition applications of recent interest. © 2008 Elsevier Ltd. All rights reserved.
Source Title: Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/50751
ISSN: 00313203
DOI: 10.1016/j.patcog.2008.07.005
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