Support vector machine using K-means clustering
- Authors
- Lee, S. J.; Park, C.; Jhun, M.; Ko, J-Y.
- Issue Date
- 3월-2007
- Publisher
- SPRINGER HEIDELBERG
- Keywords
- class imbalance; K-means clustering; support vector machine
- Citation
- JOURNAL OF THE KOREAN STATISTICAL SOCIETY, v.36, no.1, pp.175 - 182
- Indexed
- SCIE
KCI
- Journal Title
- JOURNAL OF THE KOREAN STATISTICAL SOCIETY
- Volume
- 36
- Number
- 1
- Start Page
- 175
- End Page
- 182
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/125807
- ISSN
- 1226-3192
- Abstract
- The support vector machine has been successful in many applications because of its flexibility and high accuracy. However, when a training data set is large or imbalanced, the support vector machine may suffer from significant computational problem or loss of accuracy in predicting minority classes. We propose a modified version of the support vector machine using the K-means clustering that exploits the information in class labels during the clustering process. For large data sets, our method can save the computation time by reducing the number of data points without significant loss of accuracy. Moreover, our method can deal with imbalanced data sets effectively by alleviating the influence of dominant class.
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Collections - College of Political Science & Economics > Department of Statistics > 1. Journal Articles
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