Name
Abstract | ||
|---|---|---|
Flight delay early warning can reduce the negative impact of the delay. Determining the delay grade of each interval is essentially a multi-class classification problem. This paper presents a flight delay early warning model based on a fuzzy support vector machine with weighted margin (WMSVM) , which adjust the penalties to samples and the margins between samples and the hyperplane according to the fuzzy membership to produce a more reasonable optimal hyperplane. Through one-against-one (OAO) method, the original FSVM is extended to solve multi-class classification problem .Experiments show that the method used to establish the early warning model can predict the delay grade effectively and also prove that the OAO-WMSVM has better performance than OAO-SVM. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/FSKD.2008.51 | FSKD (3) |
Keywords | Field | DocType |
fuzzy set theory,one-against-one method,negative impact,travel industry,fuzzy support vector machine,delay grade,aerospace computing,pattern classification,early warning model,fuzzy support,multi-class classification problem,vector machine,reasonable optimal hyperplane,flight delay early warning,better performance,original fsvm,weighted margin,support vector machines,fuzzy membership,multi class classification,accuracy,early warning,optimization,kernel | Kernel (linear algebra),Warning system,Distance measurement,Computer science,Support vector machine,Fuzzy logic,Fuzzy set,Artificial intelligence,Hyperplane,Fuzzy support vector machine,Machine learning | Conference |
Volume | ISBN | Citations |
3 | 978-0-7695-3305-6 | 5 |
PageRank | References | Authors |
0.44 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haiyan Chen | 1 | 35 | 3.00 |
| Jiandong Wang | 2 | 302 | 22.28 |
| Xuefeng Yan | 3 | 42 | 9.59 |