Density Clustering Based SVM and Its Application to Polyadenylation Signals

摘要：

Support vector machines (SVM) have been promising methods for classification analysis due to their solid mathematical foundations. Clustering-based SVMs are used to solve large samples classification problems and reduce the computational cost. In this paper, we present a density clustering based SVM(DCB-SVM) method to predict polyadenylation signal (PAS) in human DNA and mRNA sequences. We decrease the original data scale by using the density restricted hierarchical clustering. This strategy leads to solving smaller sized problems, making DCB-SVM work faster than standard SVM. According to the results of the PAS experiment, the proposed method is not only fast, but also shows better improvement in sensitivity than the SVM.

关键词： Support vector machines Polyadenylation signals BIRCH algorithm

作者: Yuanhai Shao Yining Feng Jing Chen Naiyang Dengt

作者单位: College of Science,China Agricultural University,Beijing 100083,China

会议类型: 国际会议

会议名称: The 3rd International Symposium on Optimization and System Biology(第三届最优化与系统生物学国际会议 OSB09)

会议地点: 张家界

会议语种:英文

页码: 117-122

在线出版日期: 2009-09-20（万方平台首次上网日期，不代表论文的发表时间）