A Study of Error-Correcting Output Codes for Structure Classification of Protein Sequence

• Main Authors: Che-Chi Wu, 吳哲奇
• Other Authors: Jyh-Jong Tasy
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/77344414623169086094

碩士 === 國立中正大學 === 資訊工程所 === 93 === Structure classification and prediction of protein sequence has been an important research theme in structural bioinformatics. Most discriminative methods in machine learning suffers the well-known "False Positives" problem due to the larger amount of folds available. In this thesis, we study new approach with multi-class classification methods to reduce the influence of the existing problem. We use the support vector machine (SVM) method as base classifiers and apply Error-Correcting Output Codes (ECOC) methods to achieve high-level multi-class classification. When scores of multiple parameter datasets are combined, majority voting reduces noise and increases recognition accuracy. The results show that our methods can obtain prediction accuracy 62.72% on a protein test dataset, where most of the proteins have below 25% sequence identity with the proteins used in training.