Comparison of Bayesian Binary Regression (BBR) and Prediction Analysis of Microarray (PAM) in Classification Problems

• Main Authors: Huang, Min-Tzu, 黃敏慈
• Other Authors: Hsiung, Chao
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/88838164886719151273

碩士 === 國立清華大學 === 統計學研究所 === 98 === Using microarray gene expression data as a tool for disease classification has been recognized as a useful method. There have been many methods proposed for analyzing these data. Among which PAM (Prediction Analysis of Microarray) is a popular method in recent years. Similar problem arose in the area of text classification and BBR (Bayesian Binary Regression) was proposed recently. In the first part of this study, we used BBR to analyze gene expression datasets and compared the performance with that of PAM. The performance is based on the error rates of both training set and testing set. The results showed that PAM and BBR have similar performance in classification. However, PAM usually used more genes than BBR. In the second part, we investigated the effect of sample size and composition of training set on the error rate of testing set. In examing the performance, we split training set according two ways: fix composition and change sample size or fix sample size and change composition. The results showed that for the same testing set, the more sample size of training set, the lower error rate. Furthermore, it is important to aware that the composition of training set to the testing set will also affect prediction performance.