@inproceedings{Shi2004,
abstract = {Protein fold recognition (PFR) is an important approach to structure discovery without relying on sequence similarity. In pattern recognition terminology, PFR is a multi-class classification problem to be solved by employing feature analysis and pattern classification techniques. This paper reformulates PFR into a multi-objective optimization problem and proposes a Multi-Objective Feature Analysis and Selection Algorithm (MOFASA). We use support vector machines as the classifier. Experimental results on the Structural Classification of Protein (SCOP) data set indicate that MOFASA is capable of achieving comparable performances to the existing results. In addition, MOFASA identifies relevant features for further biological analysis. {\textcopyright} 2004 IEEE.},
author = {Shi, Stanley Y.M. and Suganthan, P N and Deb, Kalyanmoy},
booktitle = {Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'04},
doi = {10.1109/cibcb.2004.1393933},
isbn = {0780387287},
keywords = {Feature selection,Multi-class classification,Multi-objective evolutionary algorithm,NSGA-II,Protein fold recognition,Support vector machines},
mendeley-groups = {COIN{\_}website/2004},
pages = {61--66},
title = {{Multi-class protein fold recognition using multi-objective evolutionary algorithms}},
year = {2004}
}