Wellington, New Zealand, 11-13 June, 2019
Many of industrial and research databases suffer from an unavoidable problem of data incompleteness. Nowadays, in big data era, data is generated almost everywhere: opinion polls about any topic, submarines in the deepest ocean, sensor networks in Mars, etc. As a result, the more data is collected, the more frequent the data suffers from missing values. For example, 45% of the datasets in the UCI machine learning repository, which is one of the most popular benchmark databases for machine learning, contain missing values. In many areas of application, it is not uncommon to encounter databases that have up to or even more than 50% of their entries being missing, despite the strict regulatory requirements for data collection. Behind this serious deficiency, there are a number of evident reasons, including imperfect procedures of manual data entry, incorrect measurements, and equipment errors. Missing values not only cause the non-applicability, but also cause the loss of effectiveness and efficiency of almost all data mining algorithms.
Evolutionary computation (EC) is a subfield of computational intelligence that uses computational models of evolutionary processes as the key elements in design and implementation. EC techniques have been successfully applied to solve and improve data mining tasks such as feature extraction, feature selection, discovery of classification rules, evolving regression models, clustering and association rule mining. However, EC techniques have been mainly used for data mining with complete data and also, but with less effort with missing data. Therefore, this special session aims to encourage information exchange and discussion between researchers with an interest in the use of any EC technique for handling missing data in data mining and machine learning. We encourage submissions related to theoretical advances, the development of new algorithms, as well as application-focused papers. Authors are invited to submit their original and unpublished work to this special session.
Topics of interest include but are not limited to, the use of any EC technique for handling missing data in data mining:
Cao Truong Tran School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand. email@example.com Phone: +64-4-463 5233; Fax: +64-4-463 5045.
Bing Xue School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand. Bing.Xue@ecs.vuw.ac.nz Phone: +64-4-463 5542; Fax: +64-4-463 5045.
Mengjie Zhang School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand. Mengjie.Zhang@ecs.vuw.ac.nz Phone: +64-4-463 5654; Fax: +64-4-463 5045
Cao Truong Tran received the B.Sc in applied mathematics and informatics from from Ha Noi University of Science, Vietnam, in 2005 and the M.S. degree from Le Qui Don Technical University, Viet Nam in 2009. He received the PhD degree in computer science from Victoria University of Wellington, New Zealand, in 2018. Currently, he is doing postdoc at Victoria University of Wellington.
He is researching in the field of evolutionary computation, specialized with evolutionary machine learning for data mining with missing data. He servers as a reviewer of international journals, including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, Pattern Recognition, Knowledge-Based Systems, Applied Soft Computing and Engineering Application of Artificial Intelligence. He is also a PC member of international conferences, including IEEE Congress on Evolutionary Computation, IEEE Symposium Series on Computational Intelligence, the Australasian Joint Conference on Artificial Intelligence and the AAAI Conference on Artificial Intelligence.
Bing Xue is currently a Senior Lecturer in School of Engineering and Computer Science at Victoria University of Wellington. Her research focuses mainly on evolutionary computation, feature selection, feature construction, multi-objective optimisation, data mining and machine learning. She is currently the Chair of the IEEE CIS Task Force on Evolutionary Feature Selection and Construction, and an Associate Editor/member of Editorial Board for five international journals including IEEE Computational Intelligence Magazine, Applied Soft Computing, International Journal of Swarm Intelligence, and International Journal of Computer Information Systems and Industrial Management Applications. She is a Guest Editor for the Special Issue on Evolutionary Feature Reduction and Machine Learning for the Springer Journal of Soft Computing. She is also a Guest Editor for Evolutionary Image Analysis and Pattern Recognition in Journal of Applied Soft Computing. She has been a chair for a number of international conferences including the Leading Chair of IEEE Symposium on Computational Intelligence in Feature Analysis, Selection, and Learning in Image and Pattern Recognition at SSCI 2016 and 2017, a Program Co-Chair of the 31th Australasian AI 2018, ACALCI 2018, and the 7th International Conference on SoCPaR2015, Special Session Chair for The 20th Asia-Pacific Symposium on Intelligent and Evolutionary Systems (IES2016), a Tutorial Chair for the 30th Australasian AI, and publicity chair for the international conference on Simulated Evolution And Learning (SEAL) 2017. She is the organiser of the special session on Evolutionary Feature Selection and Construction in IEEE Congress on Evolutionary Computation (CEC) 2015, 2016 and 2017, and SEAL 2014 and 2017. Dr Xue is chairing the IEEE CIS Graduate Student Research Grants Committee and the Secretary of the IEEE Chapter on Computational Intelligence in that Section.
Dr Mengjie Zhang is currently Professor of Computer Science at Victoria University of Wellington, where he heads the interdisciplinary Evolutionary Computation Research Group. He is a member of the University Academic Board, a member of the University Postgraduate Scholarships Committee, a member of the Faculty of Graduate Research Board at the University, Associate Dean (Research and Innovation) in the Faculty of Engineering, and Chair of the Research Committee of the Faculty of Engineering and School of Engineering and Computer Science. His research is mainly focused on evolutionary computation, particularly genetic programming, particle swarm optimisation and learning classifier systems with application areas of feature selection/construction and dimensionality reduction, computer vision and image processing, job shop scheduling, multi-objective optimisation, and classification with unbalanced and missing data. He is also interested in data mining, machine learning, and web information extraction. Prof Zhang has published over 400 research papers in refereed international journals and conferences in these areas. He has been serving as an associated editor or editorial board member for seven international journals including IEEE Transactions on Evolutionary Computation, the Evolutionary Computation Journal (MIT Press), Genetic Programming and Evolvable Machines (Springer), Applied Soft Computing, IEEE Transactions on Emergent Topics in Computational Intelligence, Natural Computing, and Engineering Applications of Artificial Intelligence, and as a reviewer of over 30 international journals. He has been involving major EC conferences such as GECCO, IEEE CEC, EvoStar, IEEE SSCI and SEAL as a Chair. He has also been serving as a steering committee member and a program committee member for over 100 international conferences including all major conferences in evolutionary computation. Since 2007, he has been listed as one of the top ten world genetic programming researchers by the GP bibliography (http://www.cs.bham.ac.uk/~wbl/biblio/gp-html/index.html).
Prof Zhang is the current Chair of IEEE CIS Intelligent Systems Applications, the immediate Past Chair of the IEEE CIS Emergent Technologies Technical Committee and the IEEE CIS Evolutionary Computation Technical Committee, a vice-chair of the IEEE CIS Task Force on Evolutionary Feature Selection and Construction, a vice-chair of the IEEE CIS Task Force on Evolutionary Computer Vision and Image Processing, and the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.