Prior knowledge based multi-class core vector machine for flight delay early warning

The early warning of airport runtime flight delay is a multi-class classification problem.There are two issues when solving this problem using the normal Support Vector Machine(SVM).The first issue is that the prior knowledge is not adequately utilized,and the second issue is intensive time and space consumption for data training.A new algorithm,which is called as center-constrained Minimum Enclosing Ball(MEB)based weighted margin multi-class algorithm is proposed.First,the proposed algorithm uses the prior knowledge to build a new methodology which is based on a new relative affinity function.Then this new methodology is used to calculate the weights of the sample data and add them to the SVM.After adding these features,the SVM is converted to a center-constrained MEB and can be trained easily.Experiments show that the proposed algorithm not only gives more reasonable classification results comparing to normal SVM,but also obviously speeds up the data training processing.