Hierarchical Classification with Dynamic-Threshold SVM Ensemble for Gene Function Prediction

摘要：

The paper proposes a novel hierarchical classification approach with dynamic-threshold SVM ensemble. At training phrase, hierarchical structure is explored to select suit positive and negative examples as training set in order to obtain better SVM classifiers. When predicting an unseen example, it is classified for all the label classes in a topdown way in hierarchical structure. Particulary, two strategies are proposed to determine dynamic prediction threshold for different label class, with hierarchical structure being utilized again. In four genomic data sets, experiments show that the selection policies of training set outperform existing two ones and two strategies of dynamic prediction threshold achieve better performance than the fixed thresholds.

关键词： gene function prediction hierarchical classification SVM ensemble dynamic threshold

作者: Yiming Chen Zhoujun Li Xiaohua Hu Junwan Liu

作者单位: School of Information Science and Technology, Hunan Agricultural University,Changsha, Hunan, China C Computer School of National University of Defence and Technology, Changsha,Hunan, China Computer Sch College of Information Science and Technology, Drexel University, Philadelphia, PA, 19104, USA Computer School of National University of Defence and Technology, Changsha,Hunan, China

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 336-347

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）