Name
Title | ||
|---|---|---|
Large data sets classification using convex---concave hull and support vector machine |
Abstract | ||
|---|---|---|
Normal support vector machine (SVM) is not suitable for classification of large data sets because of high training complexity. Convex hull can simplify the SVM training. However, the classification accuracy becomes lower when there exist inseparable points. This paper introduces a novel method for SVM classification, called convex---concave hull SVM (CCH-SVM). After grid processing, the convex hull is used to find extreme points. Then, we use Jarvis march method to determine the concave (non-convex) hull for the inseparable points. Finally, the vertices of the convex---concave hull are applied for SVM training. The proposed CCH-SVM classifier has distinctive advantages on dealing with large data sets. We apply the proposed method on several benchmark problems. Experimental results demonstrate that our approach has good classification accuracy while the training is significantly faster than other SVM classifiers. Compared with the other convex hull SVM methods, the classification accuracy is higher. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s00500-012-0954-x | Soft Computing - A Fusion of Foundations, Methodologies and Applications |
Keywords | Field | DocType |
Support Vector Machine, Hull, Convex Hull, Support Vector Machine Classifier, Quadratic Programming Problem | Extreme point,Structured support vector machine,Data set,Pattern recognition,Computer science,Support vector machine,Convex hull,Regular polygon,Artificial intelligence,Classifier (linguistics),Hull,Machine learning | Journal |
Volume | Issue | ISSN |
17 | 5 | 1433-7479 |
Citations | PageRank | References |
5 | 0.40 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Asdrúbal López Chau | 1 | 87 | 11.62 |
| Xiaoou Li | 2 | 550 | 61.95 |
| Wen Yu | 3 | 283 | 22.70 |