Detecting RNA sequences using two-stage SVM classifier

Authors:
Xiaoou Li;Kang Li
Affiliations:
Departamento de Computación, CINVESTAV-IPN, México D.F., México;School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK
Venue:
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
Year:
2007

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Abstract

RNA sequences detection is time-consuming because of its huge data set size. Although SVM has been proved to be useful, normal SVM is not suitable for classification of large data sets because of its high training complexity. A two-stage SVM classification approach is introduced for fast classifying large data sets. Experimental results on several RNA sequences detection demonstrate that the proposed approach is promising for such applications.