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2013 IEEE 13th International Conference on Data Mining Workshops (2012)
Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
ISBN: 978-1-4673-5164-5
pp: 431-438
Support vector machine (SVM) is not suitable for classification on large data sets due to its training complexity. Convex hull can simplify SVM training, however the classification accuracy becomes lower when there are inseparable points. This paper introduces a novel method for SVM classification, called convex-concave hull. After a grid processing, the convex hull is used to find extreme points. Then we detect a concave (non-convex) hull, the vertices of it are used to train SVM. We applied the proposed method on several problems. Experimental results demonstrate that our approach has good classification accuracy while the training is significantly faster than other state of the art methods.
Support vector machines, Training, Accuracy, Binary trees, Clustering algorithms, Vegetation, Silicon, non convex hull, SVM, convex hull
Asdrubal Lopez-Chau, Xiaoou Li, Wen Yu, "Convex-Concave Hull for Classification with Support Vector Machine", 2013 IEEE 13th International Conference on Data Mining Workshops, vol. 00, no. , pp. 431-438, 2012, doi:10.1109/ICDMW.2012.76
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