Invited Speaker---Prof. Shifei Ding


China University of Mining and Technology

Biography: He was born in Qingdao, China, in 1963, received his Ph.D degree from Shandong University of Science and Technology in 2004, and received Postdoctoral degree from Key Laboratory of Intelligent Information Processing (IIP), Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS), and his advisor was Professor Zhongzhi Shi. His research interests include Artificial Intelligence, Pattern Recognition, Machine Learning, Data Mining, and Granular Computing et al. He has published 5 books and more than 200 papers in international conferences and journals, where more than 100 papers were indexed by SCI or EI.

He is a professor and Ph.D supervisor at China University of Mining and Technology (CUMT), meanwhile he is a dean of Professional Committee of School of Computer Science and Technology, CUMT, and a dean of Joint Labortary of IIP, CUMT-CAS.

In addition, He is a senior member of China Computer Federation (CCF), a senior member of China Association for Artificial Intelligence (CAAI), a member of Professional Committee of Distributed Intelligence and Knowledge Engineering, CAAI, a member of Professional Committee of Machine Learning, CAAI, a member of Professional Committee of Rough Set and Soft Computing Professional Committee, CAAI, and a member of Professional Committee of Multi-valued Logic and Fuzzy Logic, CCF. He is a member of Editor Committee for many international journals, such as Journal of Convergence Information Technology (JCIT), International Journal of Digital Content Technology and its Applications (JDCTA) et al. Meanwhile, he is a reviewer for Journal of Information Science (JIS), Information Sciences (INS), Computational Statistics and Data Analysis (CSTA), IEEE Transactions on Fuzzy Systems (IEEE TFS), Applied Soft Computing (ASOC), Computational Statistics and Data Analysis (CSDA). International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), and served as Chairman of the NCIIP Procedure Committee, as a member of Procedure Committee for some International Conferences, such as SPND, CrC, ICMLC, et al.

Speech Title: Multiple Birth Support Vector Machine with Triplet Loss Function
Abstract: TWSVM which can be regarded as an efficient binary classification algorithm has achieved much attention. However, when it comes to multi-class classification, scholars have to consider other algorithms. Therefore, many multi-class TWSVM has been proposed to deal with multi-class problems. In this paper, we use multiple birth support vector machines(MBSVM) which is an efficient algorithm for multi-class classification in which the decision criterion is the farthest distance of the test pattern to the hyper-planes, rather than the closest distance in multi-class TWSVM. The algorithm has much lower computational complexity and can be expected to be faster than the existing multi-class SVMs. However, when facing multi-class problem of imbalanced data, the MBSVM which adopts hinge loss is easily leads to instability for resampling. To enhance the performance of the MBSVM, we present a novel MBSVM with the triplet loss (tMBSVM) which deals with the imbalanced dataset problems and shows differences between positive data and negative data in one class. Numerical experiments on data sets demonstrate the feasibility and validity of our proposed method.