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 Authors are invited to submit their original and unpublished work to this special session.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 WCCI 2024 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 WCCI 2024.
Bach Nguyen is currently a Lecturer in Artificial Intelligence at the Centre of Data Science and Artificial Intelligence (CDSAI) & School of Engineering and Computer Science, Victoria University of Wellington (VUW). He has over 30 publications 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 the Vice-Chair of the IEEE CIS Data Mining and Big Data Analytics Technical Committee, the Chair of the IEEE Task Force on Evolutionary Feature Selection and Construction, and the Chair of Young Professionals Affinity Group of IEEE New Zealand Central Section
Dr Nguyen co-chaired of IEEE Symposium on Computational Intelligence in Data Mining in IEEE Symposium on Computational Intelligence (SSCI) 2021, 2022. He was the organiser of the Special Session on Evolutionary Feature Selection, Construction, and Extraction in IEEE Congress on Evolutionary Computation in 2021, 2022, and 2023. He also organized the Special Session on Evolutionary Transfer Learning and Domain Adaptation in SSCI 2021 and 2022. He delivered a Turorial on Evolutionary Feature Reduction in CEC 2021, WCCI 2022, and CEC 2023.
Prof Bing Xue is a Fellow of IEEE, a Fellow of Engineering New Zealand, and a Professor in School of Engineering and Computer Science at Victoria University of Wellington. Her research focuses mainly on evolutionary computation, machine learning, feature selection, evolving deep neural networks, image analysis, transfer learning, multi-objective.
Prof Xue is currently the Chair of the Evolutionary Computational Technical Committee (IEEE CIS), the Chair of IEEE Task Force on Evolutionary Deep Learning and Applications, the Vice-Chair of IEEE Task Force on Evolutionary Feature Selection and Construction, and the Vice-Chair of IEEE CIS Task Force on Transfer Learning & Transfer Optimization. She is currently the Editor of the IEEE Computational Intelligence Society.
Prof Xue is the conference chair of IEEE Congress on Evolutionary Computation (CEC) 2024. She is also the organiser of the special session on Evolutionary Feature Selection and Construction in IEEE Congress on Evolutionary Computation (CEC) 2015 - 2020. She 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 2019-2022. 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 2016-2022, a Program Co-Chair of the 7th International Conference on Soft Computing and Pattern Recognition (SoCPaR2015), a Program Chair of the 31st Australasian Joint Conference on Artificial Intelligence (AI 2018), Finance Chair for 2019 IEEE Congress on Evolutionary Computation (CEC), Tutorial Co-Chair of 2022 IEEE WCCI, and Conference Chair of 2024 IEEE CEC.
She is an Editor of the IEEE Computational Intelligence Society Newsletter, 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.
Prof Yaochu Jin joins the Westlake University in August, 2023 as a Chair Professor of Artificial Intelligence. He was an Alexander von Humboldt Professor for Artificial Intelligence endowed by the German Federal Ministry of Education and Research, Faculty of Technology, Bielefeld University, Germany. He was a “Distinguished Chair Professor” in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K., a “Finland Distinguished Professor” of University of Jyvaskyla, Finland, a “Changjiang Distinguished Visiting Professor”, Northeastern University, China, and a “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. He is a Member of Academia Europaea and Fellow of IEEE.
Prof Jin is presently is the President-Elect of the IEEE Computational Intelligence Society, and the Editor-in-Chief of Complex & Intelligent Systems. He is a Member of Academia Europaea and Fellow of IEEE. He received the Outstanding Paper Award of IEEE Transactions on Evolutionary Computation (2018 and 2021) and of IEEE CIM (2015, 2017), 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 from 2019 to 2022 by the Web of Science Group.
Prof 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 and Machine Learning Research Group. He is also the Director of the CDSAI.
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 800 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).