Title :
Rare Class Classification by Support Vector Machine
Author :
He, He ; Ghodsi, Ali
Author_Institution :
Dept. of Electron. & Inf. Eng., Hong Kong Polytech. Univ., Hong Kong, China
Abstract :
The problem of classification on highly imbalanced datasets has been studied extensively in the literature. Most classifiers show significant deterioration in performance when dealing with skewed datasets. In this paper, we first examine the underlying reasons for SVM´s deterioration on imbalanced datasets. We then propose two modifications for the soft margin SVM, where we change or add constraints to the optimization problem. The proposed methods are compared with regular SVM, cost-sensitive SVM and two re-sampling methods. Our experimental results demonstrate that this constrained SVM can consistently outperform the other associated methods.
Keywords :
data analysis; pattern classification; support vector machines; optimization problem; rare class classification; resampling method; skewed dataset; support vector machine; Accuracy; Measurement uncertainty; Noise; Optimization; Support vector machines; Training; Classification; Novelty detection; Support vector machines;
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4244-7542-1
DOI :
10.1109/ICPR.2010.139