Matrix-Instance-Based One-Pass AUC Optimization
Area under the receiver operating characteristic curve,i.e.,AUC,is a widely used performance measure.Traditional off-line and some online AUC optimization methods should store the entire or part of dataset in memory which is infeasible to process big data or streaming data applications.So some scholars develop one-pass AUC optimization(OPAUC)which is independent from the data size.While OPAUC cannot process matrix instances.So we propose a matrix-instance-based one-pass AUC optimization model,i.e.,MOPAUC,to overcome such an issue.Related experiments on some benchmark datasets including five image datasets validate that MOPAUC can improve the average AUC,cost little running time with matrix-instance cases.Furthermore,some parameters including regularization parameters and weights have less influence on the average AUC while step sizes have strong influence.
One-pass Matrix instance AUC
Changming Zhu Chengjiu Mei Hui Jiang Rigui Zhou
College of Information Engineering,Shanghai Maritime University,Shanghai 201306,Peoples Republic of School of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,P
国际会议
广州
英文
527-538
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）