Prof. Sunil Vadera
Computer Science at University of Salford, UK
Sunil Vadera is a Professor of Computer
Science and the Head of the School of Computing, Science and
Engineering at the University of Salford. He is a Fellow of the
British Computer Society, a Chartered Engineer (C.Eng) and
Chartered IT Professional (CITP). He gained a first class
BSc(Hons) in Computer Science and Mathematics from the
University of Salford in 1982, receiving three best student
prizes. Following graduation, he began his career as a Research
Assistant and progressed to a Lectureship in Computer Science in
1984. He holds a PhD from the University of Manchester in the
area of Formal Methods of Software Development which was awarded
in 1992. He was promoted to a Senior Lecturer in 1997 and to a
Chair in Computer Science in 2000.
Sunil was chair of the British Computer Society Academic Accreditations Committee with responsibility for professional accreditation of all UK University programmes in Computer Science from January 2007 to December 2009. His research is driven by the desire to close the gap between theory and practice in Artificial Intelligence, with expertise in the areas of Bayesian networks, decision tree learning, credit assessment and planning.
Speech Title: From Bagging to Bandits for Cost-Sensitive Decision Tree Learning
Abstract: Decision tree learning is one of the major success stories of AI, with many data mining tools utilizing decision tree learning algorithms. Recent research in this field has been influenced by realizing that human decision making is not focused solely on accuracy, but also takes account of the potential implications of a decision. For example, a chemical engineer considers the risks of explosion when assessing the safety of a process plant, a bank manager carefully considers the implications of a customer defaulting on a loan and a medical consultant does not ignore the potential consequences of misdiagnosing a patient.
This realisation has led to significant interest in developing cost-sensitive decision tree learning algorithms. This key note presents a tour of the rich variety of cost-sensitive decision tree algorithms, aimed at illuminating the characteristics of the algorithms that will help researchers position their own work and identify gaps for future research. The key note will begin with early algorithms that make minor changes to the entropy based selection measure used in C4.5, present use of genetic algorithms to evolve cost-sensitive trees, describe the use of bagging and boosting, and conclude with recent work that explores ideas such as non-linear trees and multi-arm bandits. The presentation will be based on the authors work with colleagues and PhD students over the last decade, some of which is reported in the following publications:
• Sunil Vadera (2010), CSNL: A Cost-Sensitive Non-Linear Decision Tree Algorithm, ACM Transactions on Knowledge Discovery from Data, Vol 4, No 2, pp1-25.
• Lomax, S. and Vadera, S. (2011). An empirical comparison of cost-sensitive decision tree induction algorithms. Expert Systems, 28: 227–268
• Lomax, S. and Vadera, S. (2013). A survey of cost-sensitive decision tree induction algorithms, ACM Computing Surveys, Vol 45, No 2, pp1-35.
• Lomax, S. and Vadera, S. (2016) A Cost-Sensitive Decision Tree Learning Algorithm Based on a Multi-Armed Bandit Framework, The Computer Journal, DOI: https://doi.org/10.1093/comjnl/bxw015
• Nashnush, E. and Vadera, S. (2017) .Learning cost-sensitive Bayesian networks via direct and indirect methods, Integrated Computer-Aided Engineering, vol. 24, no. 1, pp. 17-26, 2017
Papers available from http://usir.salford.ac.uk/view/authors/13105.html
Assoc. Prof. Davy Monticolo
The ERPI Laboratory of the University of Lorraine, France
Davy Monticolo is currently Associate Professor in the ERPI Laboratory, University of Lorraine, Nancy, France. He teaches Knowledge Engineering, Web Intelligence and Agile Project Management at the Department of Innovation and Industrial Engineering. he got his HDR in December 2015 at the University of Lorraine and he received his Ph.D. (2008) in the University of Technology of Belfort-Montbéliard (France) and a M.S. (2005) degrees in the University of Savoie, France. His research interests are: Web Intelligence, Multi-Agents Systems, Knowledge Engineering and Modelling, Semantic Web and Ontologies used to design knowledge based systems. He is a member of the board of the French Research Association to promote Artificial Intelligence (AFIA). He is also the chair of the IEEE International Workshop KARE (Knowledge Acquisition Reuse & Evaluation) since 2008.
He is the chair of the IEEE KARE (Knowledge Acquisition Reuse and Evaluation) international workshop. He is also in the board of the AFIA (French Research Group of Artificial Intelligence).
Speech Title: Knowledge Management inside open communities on the Web, the intersection between semantic and intelligent web systems
Abstract: The Web became a virtual place where persons, professional actors constitute online organizations and open communities using software and applications to interact and exchange know-how and knowledge. The huge volume of data (text, posts, tags, images, videos, etc.) created by those communities has to be exploited to make better and more informed decisions that could help solving global societal problems. To achieve this goal we need to build intelligent knowledge based systems and recommendations systems to handle the data and to make the link between humans and machines by taking into account of the ethics rules. These interactions between humans and machines create many issues, the one is to build efficient intelligent web architectures capable to manage complex human organizations and also to bridge the gap between the social semantics generate by humans and formal semantics used by computers.