Title :
A Novel Measure of Diversity for Support Vector Machine Ensemble
Author :
Li, Kai ; Gao, Hongtao
Author_Institution :
Sch. of Math. & Comput., Hebei Univ., Baoding, China
Abstract :
The diversity of an ensemble is deemed to be a key factor which determines performance in ensemble learning. A variety of approaches have been advanced to quantify diversity by analyzing the prediction of classification which relies on the validation set. This paper proposes a new method how to measure diversity and ensemble for linear kernel Support Vector Machine, which is based on the characteristic parameters of Support Vector Machine. The new method is proved to achieve better performance than the traditional measures of diversity such as Discrepancy method. Further research on relationship between diversity and accuracy is conducted by the method.
Keywords :
learning (artificial intelligence); pattern classification; support vector machines; classification prediction; discrepancy method; ensemble learning; linear kernel support vector machine; support vector machine ensemble; Area measurement; Computer security; Informatics; Information security; Information technology; Kernel; Learning systems; Machine learning; Support vector machine classification; Support vector machines; Support Vector Machine(SVM); diversity; ensemble;
Conference_Titel :
Intelligent Information Technology and Security Informatics (IITSI), 2010 Third International Symposium on
Conference_Location :
Jinggangshan
Print_ISBN :
978-1-4244-6730-3
Electronic_ISBN :
978-1-4244-6743-3
DOI :
10.1109/IITSI.2010.136