Title :
The Technology of Selective Multiple Classifiers Ensemble Based on Kernel Clustering
Author :
Xian-yi, Cheng ; Hong-ling, Guo
Author_Institution :
Sch. of Comput. Sci. & Telecommun. Eng., Jiangsu Univ., Zhengjiang
Abstract :
Because of the high request to classifiers performance of people and the implementation complexity of multiple classifiers ensemble approach, this paper proposes a new method of selective multiple classifiers according to the distribution characteristic of classifiers, the classifying performance as well as the existence diversity. The algorithm uses the Kernel-based clustering method to estimate the performance of each classifier in the whole feature space .And we choose the classifiers which has diversity to form the last ensemble classifier set according to that each classifier has diversity. We carry on contrasting experiments to compare the method which we propose with the bagging method and the best method in the ELENA data set. From the result of theoretic analysis and experiment, we could see that the classifiers ensemble method is efficient in pattern recognition field.
Keywords :
pattern classification; pattern clustering; ELENA data set; existence diversity; kernel clustering; pattern recognition; selective multiple classifiers ensemble; Algorithm design and analysis; Application software; Clustering algorithms; Clustering methods; Computer science; Information technology; Kernel; Pattern analysis; Pattern recognition; Space technology; Kernel-based Clustering; Multiple classifiers combination; Neural networks; classifier selection;
Conference_Titel :
Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-0-7695-3497-8
DOI :
10.1109/IITA.2008.23