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Weighted Support Vector Machine Using k-Means Clustering

Authors
Bang, SungwanJhun, Myoungshic
Issue Date
26-11월-2014
Publisher
TAYLOR & FRANCIS INC
Keywords
Classification; Class imbalance; k-means clustering; Recovery process; Weighted support vector machine
Citation
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v.43, no.10, pp.2307 - 2324
Indexed
SCIE
SCOPUS
Journal Title
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Volume
43
Number
10
Start Page
2307
End Page
2324
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/96735
DOI
10.1080/03610918.2012.762388
ISSN
0361-0918
Abstract
The support vector machine (SVM) has been successfully applied to various classification areas with great flexibility and a high level of classification accuracy. However, the SVM is not suitable for the classification of large or imbalanced datasets because of significant computational problems and a classification bias toward the dominant class. The SVM combined with the k-means clustering (KM-SVM) is a fast algorithm developed to accelerate both the training and the prediction of SVM classifiers by using the cluster centers obtained from the k-means clustering. In the KM-SVM algorithm, however, the penalty of misclassification is treated equally for each cluster center even though the contributions of different cluster centers to the classification can be different. In order to improve classification accuracy, we propose the WKM-SVM algorithm which imposes different penalties for the misclassification of cluster centers by using the number of data points within each cluster as a weight. As an extension of the WKM-SVM, the recovery process based on WKM-SVM is suggested to incorporate the information near the optimal boundary. Furthermore, the proposed WKM-SVM can be successfully applied to imbalanced datasets with an appropriate weighting strategy. Experiments show the effectiveness of our proposed methods.
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