A New Approach to Multi-class SVM-Based Classification Using Error Correcting Output Codes

Abstract

Protein fold classification is the prediction of protein’s tertiary structure (fold) from amino acid sequence without relying on the sequence similarity. The problem how to predict protein fold from amino acid sequence is regarded as a great challenge in computational biology and bioinformatics. To deal with this problem the support vector machine (SVM) classifier was introduced. However the SVM is a binary classifier, but protein fold recognition is a multi-class problem. So the method of solving this issue was proposed based on error correcting output codes (ECOC). The key problem in this approach is how to construct the optimal ECOC codewords. There are three strategies presented in this paper based on recognition ratios obtained by binary classfiers on the traing data set. The SVM classifier using the ECOC codewords contructed using these strategies was used on a real world data set. The obtained results (57.1% - 62.6%) are better than the best results published in the literature.