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

Hailong ZHU, Hong Qiang Wang

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
Pages531-534
Number of pages4
DOIs
Publication statusPublished - 2010
Event2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010 - HongKong, China
Duration: 18 Dec 201021 Dec 2010

Publication series

Name2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010

Conference

Conference2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
Country/TerritoryChina
CityHongKong
Period18/12/1021/12/10

Scopus Subject Areas

  • Biomedical Engineering
  • Health Informatics

User-Defined Keywords

  • Data structure
  • Feature relation network
  • Pattern classification

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