Hierarchical classification with dynamic-threshold SVM ensemble for gene function prediction

Authors:
Yiming Chen;Zhoujun Li;Xiaohua Hu;Junwan Liu
Affiliations:
School of Information Science and Technology, Hunan Agricultural University, Changsha, Hunan, China and Computer School of National University of Defence and Technology, Changsha, Hunan, China;Computer School of National University of Defence and Technology, Changsha, Hunan, China and Computer School of BeiHang University BeiJing, China;College of Information Science and Technology, Drexel University, Philadelphia, PA;Computer School of National University of Defence and Technology, Changsha, Hunan, China
Venue:
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Year:
2010

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Abstract

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 top-down 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.