http://dx.doi.org/10.1109/BIBMW.2010.5703857">
 

Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Feature relation network that can identify underlying data structure for effective pattern classification

Language

English

Abstract

This paper proposes a feature relation network (FRN) to model the underlying feature relation structures of a set of observations. A pattern classification system is then constructed based on the feature relation network, namely PCS-FRN. During training process, PCS-FRN will form an attractor for each group of samples in order to lower the overall energy states. The attractor, or a feature relation network, reflects the underlying data structure that can discriminate different classes. Parameters of PCS-FRN are estimated by the multi-dimensional evolutionary algorithm. The PCS-FRN system was tested on a synthetic dataset and three real-world medical datasets and compared with conventional classification techniques. Experiment results show that PCS-FRN can achieve better classification accuracies on both binary and multi-class problems. ©2010 IEEE.

Keywords

Data structure, Feature relation network, Pattern classification

Publication Date

2010

Source Publication Title

2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops

Start Page

531

End Page

534

Conference Location

Hong Kong, China

Publisher

IEEE

ISBN (print)

9781424483037

ISBN (electronic)

9781424483044

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