Plenary Lecture
Decision Making Based on Dempster-Shafer Theory of Evidence
Associate Professor Mohammad Abdullah-Al-Wadud
Department of Industrial and Management Engineering
Hankuk University of Foreign Studies
South Korea
E-mail: wadud@hufs.ac.kr
Abstract: The available training data for different classification problems are usually imprecise and incomplete, which leads to uncertainty in classifications as traditional probability-based classifiers requires complete knowledge of priors and conditional probabilities. This requires a robust fusion framework to combine available information sources with some degree of certainty. The Dempster–Shafer theory of evidence provides a method for combining evidences from different sources without prior knowledge of their distributions. In this method, it is possible to assign probability values to sets of possibilities rather than to single events only, and it is not needed to distribute all the probability values among the events, thus modeling more naturally certain classes of problems. Dempster’s rules for combination give a numerical procedure for fusing together multiple pieces of measurements from different (unreliable) observers. This talk addresses the employment of the Dempster-Shafer Theory of evidence in few practical applications.
Brief Biography of the Speaker: M. Abdullah-Al-Wadud received his B.S. degree in Computer Science and M.S. in Computer Science and Engineering from the University of Dhaka, Bangladesh in 2003 and 2004, respectively. He served as a lecturer in Faculty of Sciences and Information Technology, Daffodil International University, Bangladesh, and in Faculty of Sciences and Engineering, East West University, Bangladesh, in 2003 and 2004, respectively. In 2009, he completed his PhD in Computer Engineering from Kyung Hee University, South Korea. Since then he has been a faculty member of the Department of Industrial and Management Engineering, Hankuk University of Foreign Studies, South Korea, serving as a full-time lecturer from 2009 to 2011, an assistant professor from 2011 to 2013, and an associate professor since 2013. His research interest includes image enhancement, pattern recognition, sensor and ad hoc networks. He is author of 50 papers published in different peer reviewed international journals and conference proceedings.