DocumentCode
578158
Title
Imbalanced data classification algorithm based on hybrid model
Author
Yu, Xiang ; Zhang, Xiaolong
Author_Institution
Sch. of Comput. Sci. & Technol., Wuhan Univ. of Sci. & Technol., Wuhan, China
Volume
2
fYear
2012
fDate
15-17 July 2012
Firstpage
735
Lastpage
740
Abstract
This paper proposes a method to deal with imbalanced data in classification. Particle of Swarm Optimization (PSO) algorithm is used to optimize the SVM parameters, and the optimized SVM is used as weak classifier for AdaBoost inside cascade model. The experimental results show that the method significantly improves the overall classification accuracy and the recognition rate of the rare class.
Keywords
data analysis; particle swarm optimisation; support vector machines; AdaBoost; PSO algorithm; cascade model; hybrid model; imbalanced data classification algorithm; optimized SVM; particle of swarm optimization algorithm; rare class; recognition rate; Abstracts; Adaptation models; Computational modeling; Ionosphere; Optimization; AVC; Algorithm optimization; Boosting Algorithm; Cascade Model; PSO; SVM;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics (ICMLC), 2012 International Conference on
Conference_Location
Xian
ISSN
2160-133X
Print_ISBN
978-1-4673-1484-8
Type
conf
DOI
10.1109/ICMLC.2012.6359016
Filename
6359016
Link To Document