Hierarchical fuzzy classifier for bioinformatics data

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.