Special Session on Evolutionary Computation for Feature Selection, Extraction, and Dimensionality Reduction

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 features from the original feature set. Feature extraction or construction aims to create a set of effective, high-level features. Dimensionality reduction aims to reduce the data space dimensionality, mitigating the “curse of dimensionality.”

The theme of this special session is the use of evolutionary computation for feature reduction, covering all evolutionary computation paradigms. Authors are invited to submit original and unpublished work on theories, methods, and applications related to feature selection, extraction, and dimensionality reduction.

• Dimensionality reduction and feature ranking/weighting
• Feature subset selection and multi-objective feature selection
• Filter, wrapper, and embedded methods for feature selection and extraction
• Hybrid evolutionary computation with neural networks, fuzzy systems, and machine learning
• Applications in image analysis, biomarker detection, text mining, finance, and more

• Paper Submission Deadline: January 31, 2026 (STRICT DEADLINE)
• Notification of Acceptance: March 31, 2026
• Final Paper Submission: April 30, 2026

Please follow the IEEE CEC 2025 Submission Website. Special session papers are treated the same as regular conference papers. Please specify that your paper is submitted to the special session on Evolutionary Computation for Feature Selection, Extraction and Dimensionality Reduction. All accepted papers will be published in IEEE Xplore.

The program committee includes leading researchers from around the world:

• Dr Bach Nguyen, Victoria University of Wellington, New Zealand — Hoai.Bach.Nguyen@ecs.vuw.ac.nz
• Prof Bing Xue, Victoria University of Wellington, New Zealand — Bing.Xue@ecs.vuw.ac.nz
• Prof Mengjie Zhang, Victoria University of Wellington, New Zealand — Mengjie.Zhang@ecs.vuw.ac.nz

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Biographies

Bach Nguyen is a Postdoctoral Research Fellow at Victoria University of Wellington. His research focuses on feature selection, feature construction, evolutionary computation, and transfer learning. He serves as Vice-Chair of the IEEE Data Mining and Big Data Analytics Technical Committee and Chair of the IEEE Task Force on Evolutionary Feature Selection and Construction.

Bing Xue is a Professor in the School of Engineering and Computer Science at Victoria University of Wellington. Her research interests include evolutionary computation, machine learning, evolving neural networks, image analysis, and multi-objective optimization. She serves as Vice-Chair of several IEEE CIS Task Forces and Associate Editor of multiple international journals.

Mengjie Zhang is a Fellow of the Royal Society of New Zealand and IEEE, and Professor at Victoria University of Wellington. His research spans evolutionary computation, computer vision, feature construction, and deep learning. He has served as Associate Editor for over ten journals and chaired multiple international conferences including IEEE CEC and SEAL.