| In this thesis, we address the problem of protein localization classification with the performance measured mainly on precision of the outer membrane protein prediction. We apply the technique of association-rule based classification and propose several important optimization techniques in order to speed up the rule-mining process. In addition, we introduce the framework of building classifiers with multiple levels, which we call the refined classifier , in order to further improve the classification performance on top of the single-level classifier. Our experimental results show that our algorithms are efficient and produce high precision while maintaining the corresponding recall at a good level. Also, the idea of refined classification indeed improves the performance of the final classifier. Furthermore, our classification rules turn out to be very helpful for biologists to improve their understanding of functions and structures of the outer membrane proteins. (Abstract shortened by UMI.)... |
