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 Authors are invited to submit their original and unpublished work to this special session.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 CEC2022 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 CEC2022 will be included in the conference proceedings published by IEEE Explore, which are typically indexed by EI.
Bach Nguyen is a Postdoctoral Research Fellow in the School of Engineering and Computer Science at Victoria University of Wellington. His research focuses mainly on feature selection/feature construction, evolutionary computation, and transfer learning.
Dr Nguyen is the Vice-Chair of the IEEE Data Mining and Big Data Analytics Technical Committee and the Chair of the IEEE Task Force on Evolutionary Feature Selection and Construction in IEEE Computational Intelligence Society (IEEE CIS). He co-chaired of IEEE Symposium on Computational Intelligence in Data Mining in IEEE Symposium on Computational Intelligence (SSCI) 2021. He organised Special Session on Evolutionary Feature Selection, Construction, and Extraction and delivered a Tutorial on Evolutionary Feature Reduction in IEEE Congress on Evolutionary Computation (IEEE CEC) 2021.
Bing Xue is 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 a Vice-Chair (and founding chair) of IEEE Task Force on Evolutionary Feature Selection and Construction, of IEEE Task Force on Transfer Learning & Transfer Optimization, and of IEEE Task Force on Evolutionary Deep Learning and Applications in IEEE CIS. She is a co-chair of the Special Session on Evolutionary Feature Selection and Construction in IEEE CEC 2015-2021. Prof Xue has been a chair for a number of international conferences including CEC and GECCO. She is an Associate Editor or Member of the Editorial Board for seven international journals including IEEE Transactions of Evolutionary Computation.
Yaochu Jin is an Alexander von Humboldt Professor for Artificial Intelligence endowed by the German Federal Ministry of Education and Research, Faculty of Technology, Bielefeld University, Germany. 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 the Editor-in-Chief of the IEEE Transactions on Cognitive and Developmental Systems and the Editor-in-Chief of Complex & Intelligent Systems. He is an Associate Editor or Editorial Board Member of 6 international journals. Prof Jin received the Outstanding Paper Award of IEEE TEVC (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 for 2019 and 2020 by the Web of Science Group.
Mengjie Zhang is a Fellow of Royal Society of New Zealand, a Fellow of IEEE, and a Professor at Victoria University of Wellington (VUW). He is Associate Dean in the Faculty of Engineering, VUW. His research is mainly focused on evolutionary computation, feature selection/construction, computer vision and image processing, evolutionary deep learning and transfer learning, job shop scheduling, multi-objective optimisation.
Prof Zhang has been served as an associated editor or editorial board member for over 10 international journals. He has been a major chair for 8 international conferences. Since 2014, he has been co-chairing the Special Session on Evolutionary Feature Selection and Construction at IEEE CEC and SEAL. 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 3 IEEE CIS TFs; and also the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.