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 Authors are invited to submit their original and unpublished work to this special session.
Please follow the IEEE CEC2021 Submission Web Site. Special session papers are treated the same as regular conference papers. Please specify that your paper is submitted to SS Evolutionary Computation for Feature Selection, Extraction and Dimensionality Reduction . All papers accepted and presented at IEEE CEC2021 will be included in the conference proceedings published by IEEE Explore, which are typically indexed by EI.
Bach Nguyen is currently a Postdoctoral Research Fellow in School of Engineering and Computer Science at VUW. He has over 20 papers published in fully refereed international journals and conferences. His research focuses mainly on evolutionary computation, machine learning, classification, feature selection, transfer learning, and multi-objective optimisation.
Dr Nguyen is currently chairing the IEEE Task Force on Evolutionary Feature Selection and Construction and the Young Professionals of IEEE New Zealand Central Section.
Dr Nguyen has been serving as a program committee member for over 10 international conferences including AAAI, IJCAI, IEEE CEC, GECCO, and IEEE SSCI. He has been serving as a reviewer for over 10 international journals including IEEE Transactions on Evolutionary Computation and IEEE Transactions on Cybernetics.
Bing Xue is currently an Associate Professor and Program Director of Science in School of Engineering and Computer Science at VUW. She has over 200 papers published in fully refereed international journals and conferences and her research focuses mainly on evolutionary computation, machine learning, classification, symbolic regression, feature selection, evolving deep neural networks, image analysis, transfer learning, multi-objective machine learning.
Dr Xue is currently the Chair of IEEE Computational Intelligence Society (CIS) Data Mining and Big Data Analytics Technical Committee, and Vice-Chair of IEEE Task Force on Evolutionary Feature Selection and Construction, Vice-Chair of IEEE CIS Task Force on Transfer Learning & Transfer Optimization, and of IEEE CIS Task Force on Evolutionary Deep Learning and Applications.
A/Prof Xue is the organiser of the special session on Evolutionary Feature Selection and Construction in IEEE Congress on Evolutionary Computation (CEC) 2015, 2016, 2017, 2018 2019, and 2020. A/Prof Xue has been a chair for a number of international conferences including the Chair of Women@GECCO 2018 and a co-Chair of the Evolutionary Machine Learning Track for GECCO 2019 and 2020. She is the Lead Chair of IEEE Symposium on Computational Intelligence in Feature Analysis, Selection, and Learning in Image and Pattern Recognition (FASLIP) at SSCI 2016, 2017,2018, 2019 and 2020, a Program Co-Chair of the 7th International Conference on Soft Computing and Pattern Recognition (SoCPaR2015), a Program Chair of the 31th Australasian Joint Conference on Artificial Intelligence (AI 2018), and Finance Chair for 2019 IEEE Congress on Evolutionary Computation.
She is an Associate Editor or Member of the Editorial Board for seven international journals, including IEEE Transactions of Evolutionary Computation, IEEE Computational Intelligence Magazine, and ACM Transactions on Evolutionary Learning and Optimisation.
Yaochu Jin is currently a Distinguished Chair, Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K., where he heads the Nature Inspired Computing and Engineering Group. He was a “Finland Distinguished Professor” of University of Jyvaskyla, Finland, a “Changjiang Distinguished Visiting Professor”, Northeastern University, China, and “Distinguished Visiting Scholar”, University of Technology Sydney, Australia. His main research interests include data-driven surrogate-assisted evolutionary optimization, secure machine learning, multi-objective evolutionary learning, swarm robotics, and evolutionary developmental systems.
Dr Jin is presently the Editor-in-Chief of the IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS and the Editor-in-Chief of Complex & Intelligent Systems. He was an IEEE Distinguished Lecturer, and Vice President of the IEEE Computational Intelligence Society. He was the General Co-Chair of the 2016 IEEE Symposium Series on Computational Intelligence and the 2020 IEEE Congress on Evolutionary Computation. He is the recipient of the 2018 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, the 2015, 2017, and 2020 IEEE Computational Intelligence Magazine Outstanding Paper Award, and the Best Paper Award of the 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. He is recognized as a Highly Cited Researcher 2019 by the Web of Science Group. He is a Fellow of IEEE.
Mengjie Zhang is a Fellow of Royal Society of New Zealand, a Fellow of IEEE, a Panel Member of the Marsden Fund (New Zealand Government Funding), 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, 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, evolutionary deep learning and transfer learning, job shop scheduling, multi-objective optimisation, and clustering 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 500 research papers in refereed international journals and conferences in these areas. He has been serving as an associated editor or editorial board member for over 10 international journals including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, the Evolutionary Computation Journal (MIT Press), ACM Transactions on Evolutionary Learning and Optimisation, Genetic Programming and Evolvable Machines (Springer), IEEE Transactions on Emergent Topics in Computational Intelligence, Applied Soft Computing, and Engineering Applications of Artificial Intelligence, and as a reviewer of over 30 international journals. He has been a major chair for 8 international conferences. He has also been serving as a steering committee member and a program committee member for over 80 international conferences including all major conferences in evolutionary computation. Since 2007, he has been listed in the top five world genetic programming researchers by the GP bibliography (http://www.cs.bham.ac.uk/~wbl/biblio/gp-html/index.html).
He is the Tutorial Chair for GECCO 2014, an AIS-BIO Track Chair for GECCO 2016, an EML Track Chair for GECCO 2017, and a GP Track Chair for GECCO 2020. Since 2012, he has been co-chairing several parts of IEEE CEC, SSCI, and EvoIASP/EvoApplications conference (he has been involving major EC conferences such as GECCO, CEC, EvoStar, SEAL). Since 2014, he has been co-organising and co-chairing the special session on evolutionary feature selection and construction at IEEE CEC and SEAL, and also delivered a keynote/plenary talk for IEEE CEC 2018, IEEE ICAVSS 2018, DOCSA 2019, IES 2017 and Chinese National Conference on AI in Law 2017.
Prof Zhang was the Chair of the IEEE CIS Intelligent Systems Applications, 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, the IEEE CIS Task Force on Evolutionary Computer Vision and Image Processing, and the IEEE CIS Task Force on Evolutionary Deep Learning and Applications; and also the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.