19 - 24th July, 2020, Glasgow (UK)
In machine learning and data mining, the quality of the input data determines the quality of the output (e.g. accuracy), known as the GIGO (Garbage In, Garbage Out) principle. For a given problem, the input data of a learning algorithm is almost always expressed by a number of features (attributes or variables). Therefore, the quality of the feature space is a key for success of any machine learning and data algorithm.
Feature selection, feature extraction or construction and dimensionality reduction are important and necessary data pre-processing steps to increase the quality of the feature space, especially with the trend of big data. Feature selection aims to select a small subset of important (relevant) features from the original full feature set. Feature extraction or construction aims to extract or create a set of effective features from the raw data or create a small number of (more effective) high-level features from (a large number of) low-level features. Dimensionality reduction aims to reduce the dimensionality of the data space with the focus of solving "the curse of dimensionality" issue. All of them can potentially improve the performance of a learning algorithm significantly in terms of the accuracy, increase the learning speed, and the complexity and the interpretability of the learnt models. However, they are challenging tasks due to the large search space and feature interaction problems. Recently, there has been increasing interest in using evolutionary computation techniques to solve these tasks due to the fast development of evolutionary computation and capability of stochastic search, constraint handling and dealing with multiple conflict objectives.
The theme of this special session is the use of evolutionary computation for feature reduction, covering ALL different evolutionary computation paradigms. The aim is to investigate both the new theories and methods in different evolutionary computation paradigms to feature selection, feature extraction and construction, dimensionality reduction and related studies on improving quality of the feature space, and their applications. Authors are invited to submit their original and unpublished work to this special session.
Topics of interest include but are not limited to:
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.
Yaochu Jin Department of Computing, University of Surrey, United Kingdom. email@example.com Phone: +44-1483-686037; Fax: +44-1483-686051
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
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, Vice Chair of the IEEE CIS Data Mining and Big Data Analytics Technical Committee, 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, 2017 and 2018, 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, 2017 and 2018, 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.
Yaochu Jin is Professor in Computational Intelligence, Head of the Nature Inspired Computing and Engineering (NICE) group, Co-Coordinator of the Centre for Mathematical and Computational Biology (CMCB), Department of Computer Science, University of Surrey. Prof Jin is also a Finland Distinguished Professor (2015-17) with the Industrial Optimization Group, Department of Mathematical Information, University of Jyvaskyla, Finland, and a Changjiang Distinguished Visiting Professor (2015-17), State Key Laboratory of Synthetical Automation of Process Industry, Northeastern University, China.
Prof Jin is an IEEE Fellow for contributions to evolutionary optimization. He is also the Editor-in-Chief of the IEEE Transactions on Cognitive and Developmental Systems and Co-Editor-in-Chief of Complex & Intelligent Systems (Springer), AdCom member (2012-2013), Vice President for Technical Activities (2014-2015), and an IEEE Distinguished Lecturer (2013-2015) of the IEEE Computational Intelligence Society. In addition, Prof Jin is an Associate Editor of the IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, IEEE Transactions on Nanobioscience, Soft Computing (Springer), and BioSystems (Elsevier). Prof Jin is also an Editorial Board Member of the Evolutionary Computation Journal (MIT Press) and Natural Computing (Springer). He is a past Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, and the IEEE Transactions on Control Systems Technology, IEEE Transactions on Autonomous Mental Development, and IEEE Computational Intelligence Magazine. Prof Jin is a Member of EPSRC Peer Review College and EPSRC ICT Responsive Mode Panel, a Panel Member of EC FP7 FET/HBP grants, a Panel Review Member of Academy of Finland and a Reviewer of VQR (Research Assessment), Italy. He is also the General Co-Chair of the 2016 IEEE Symposium Series on Computational Intelligence (SSCI 2016), Founding General Co-Chair of the IEEE Symposium on Computational Intelligence in Big Data, IEEE Symposium on Computational Intelligence in Multi-Criterion Decision-Making (IEEE MCDM), IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (IEEE CIDUE). Prof Jin was General Chair of 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB 2012) and Program Chair of 2013 IEEE Congress on Evolutionary Computation.
Dr Mengjie Zhang is a Fellow of Royal Society New Zealand, and 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.