Hierarchical fuzzy classifier for bioinformatics data

A. Chong, T. D. Gedeon, L. T. Koczy

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

In this research, a preliminary study of the application of hierarchical fuzzy rule-based classifier for protein secondary structure prediction has been carried out. The use of a hierarchical structured rulebase alleviates, to some extent, the problem of rule explosions that has prevented the use of traditional fuzzy system in many biomedical related problems. As part of the study, a hierarchical fuzzy classifier was built from a set of training data. Although the accuracy of the classifier is far from comparable to the current established techniques, the experiment has successfully confirmed the feasibility of the application of the hierarchical classifier for protein structure prediction. This calls for further research to further improve the accuracy of the rule-based classifier. The advantages of using the rule-based classifier as compared to other artificial intelligent techniques for protein structure prediction are also discussed in the paper.

Original languageEnglish
Title of host publicationProceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003
PublisherIEEE Computer Society
Pages45-48
Number of pages4
ISBN (Print)0780379462, 9780780379466
DOIs
Publication statusPublished - 2003
Externally publishedYes
Event7th International Symposium on Signal Processing and Its Applications, ISSPA 2003 - Paris, France
Duration: 1 Jul 20034 Jul 2003

Publication series

NameProceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003
Volume2

Conference

Conference7th International Symposium on Signal Processing and Its Applications, ISSPA 2003
Country/TerritoryFrance
CityParis
Period1/07/034/07/03

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