Department of Computer Science
Feature relation network that can identify underlying data structure for effective pattern classification
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.
Data structure, Feature relation network, Pattern classification
Source Publication Title
2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops
Hong Kong, China
Link to Publisher's Edition
Zhu, Hai Long, and Hong Qiang Wang. "Feature relation network that can identify underlying data structure for effective pattern classification." 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (2010): 531-534.