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
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;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2012 International Conference on
Conference_Location :
Xian
Print_ISBN :
978-1-4673-1484-8
DOI :
10.1109/ICMLC.2012.6359016
