IEEE Taskforce ESFC

IEEE Taskforce on Evolutionary Computation for Feature Selection and Construction

IEEE Computational Intelligence Society

Evolutionary Computation Technical Committee

Welcome

Welcome to the website of the IEEE Taskforce on Evolutionary Feature Selection and Construction. We are passionate to organise events and activities on evolutionary feature selection and construction, to create opportunities for researchers and industrial practitioners to share ideas, seek for collaborations, and make friends together!

If you are interested in joining this taskforce or receiving updates from us, please feel free to contact Bach Hoai Nguyen.

NEWS

[IEEE Fellow]: Prof. Bing Xue, our Vice-chair, has been awarded IEEE Fellow, effective from Jan. 2024.
[Call for paper]: Special Session on Evolutionary Computation for Feature Selection, Extraction and Dimensionality Reduction, Bach Hoai Nguyen, Bing Xue, Yaochu Jin, Mengjie Zhang, IEEE World Congress on Computational Intelligence , 30 June – 5 July 2024, Yokohama, Japan.
[Tutorial/Seminar]: Tutorial on 'Evolutionary Multi-objective Feature Selection for Machine Learning', Ruwang Jiao, Bing Xue, Mengjie Zhang, IEEE SSCI 2023 .

Scope

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.

Many real-world problems involve a large number of features/variables, which leads to the problem known as "the curse of dimensionality". However, not all features are essential since many of them are redundant or irrelevant, and the useful features are typically not equally important. This problem can be solved by feature selection to select a small subset of original features, or feature construction to construct a smaller set of high-level features using the original low-level features and mathematical or logical operators. Feature selection and construction are challenging tasks because of the large search space and feature interaction problems. Due to the powerful search abilities and/or flexible solution encoding/representation schemes, there has been increasing interest in using evolutionary computation (EC) techniques to solve feature selection and construction problems. However, the dimensionality and the complexity of the data in real-world problems grows fast in recent years, which requires novel effective and efficient approaches to addressing new challenges in this area.

Mission

Feature selection and construction are important tasks in many areas, such as Data Mining, Machine Learning, Image Processing and Analysis, Statistics, Operation Research, Biology, Engineering, Finance, and Business. Researchers from these areas have started investigating EC techniques to solve feature selection and construction problems, but these researchers attend different events and activities. This task force would be an outstanding platform for them to share knowledge, exchange ideas, transfer tools, and generate new research lines.

The objectives of this task force are:

To promote the applications of EC techniques to address feature selection and construction tasks in different areas.
To facilitate collaboration between researchers from related disciplines, such as Data Mining, Machine Learning, Statistics, Operation Research, Biology, Engineering, Finance, Business, Image Processing and Analysis, Classification, Clustering, Regression, Medical and Health Care, Networks, and Security.
To promote discussions and connections between researchers, industrialists, and practitioners.

Anticipated interests

The theme of this task force is EC for feature selection and construction, covering all different EC paradigms. Topics of interest include but are not limited to:

Feature ranking/weighting, subset selection, construction, and learning
Novel fitness evaluation criteria in feature selection, construction, and learning
Filter, wrapper, and embedded approaches to feature selection, construction, and learning
Evolutionary deep feature learning
Single objective and multi-objective feature selection and construction
Theoretical analysis on evolutionary feature selection and construction algorithms
Feature extraction/construction in images and video sequences
Feature selection and construction on high-dimensional and large-scale data
EC for feature selection and construction in real-world applications
Feature selection, extraction, and dimensionality reduction in image analysis, pattern recognition, classification, clustering, regression, and other tasks
Feature selection, extraction, and dimensionality reduction on high-dimensional and large-scale data
Analysis on evolutionary feature selection, extraction, and dimensionality reduction algorithms
Hybridisation of evolutionary computation and neural networks, and fuzzy systems for feature selection and extraction
Hybridisation of evolutionary computation and machine learning, information theory, statistics, mathematical modelling, etc., for feature selection and extraction
Real-world applications of evolutionary feature selection and extraction, e.g. images and video sequences/analysis, face recognition, gene analysis, biomarker detection, medical data classification, diagnosis, and analysis, hand written digit recognition, text mining, instrument recognition, power system, financial and business data analysis, et al.

Past Events and Activities

Special session on Evolutionary Computation for Feature Selection, Extraction and Dimensionality Reduction in IEEE Congress on Evolutionary Computation (CEC2023)
Tutorial on Evolutionary Feature Reduction for Machine Learning in IEEE Congress on Evolutionary Computation (CEC2023)
Tutorial on Evolutionary Computation for Feature Selection and Feature Construction in The Genetic and Evolutionary Computation Conference (GECCO2023)
Special session on Evolutionary Computation for Feature Selection, Extraction and Dimensionality Reduction in IEEE World Congress on Computational Intelligence (WCCI 2022)/IEEE Congress on Evolutionary Computation (CEC2022)
Tutorial on Evolutionary Feature Reduction for Machine Learning in IEEE World Congress on Computational Intelligence (WCCI 2022)/IEEE Congress on Evolutionary Computation (CEC2022)
Tutorial on Evolutionary Computation for Feature Selection and Feature Construction in The Genetic and Evolutionary Computation Conference (GECCO2022)
Invited talk on "Evolutionary Computation for Feature Reduction" by Dr. Bach Nguyen in IEEE International Conference on Data Mining and Machine Learning Workshop (IEEE ICDMW 2021)
IEEE Symposium on Computational Intelligence in Feature Analysis, Selection, and Learning in Image and Pattern Recognition (FASLIP) [ Call for Papers] in IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021)
Tutorial on Evolutionary Computation for Feature Reduction in IEEE Congress on Evolutionary Computation (CEC2021)
Tutorial on Evolutionary Computation for Feature Selection and Feature Construction in The Genetic and Evolutionary Computation Conference (GECCO2021)
Special session on Evolutionary Computation for Feature Selection, Extraction and Dimensionality Reduction in IEEE Congress on Evolutionary Computation (CEC2021)
IEEE Symposium on Computational Intelligence in Feature Analysis, Selection, and Learning in Image and Pattern Recognition (FASLIP) [ Call for Papers] in IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2020)
IEEE World Congress on Computational Intelligence (WCCI 2020)
The Genetic and Evolutionary Computation Conference (GECCO 2020)
IEEE Symposium on Computational Intelligence in Feature Analysis, Selection, and Learning in Image and Pattern Recognition (FASLIP) [ Call for Papers] in IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2019)
Special session on Evolutionary Computation for Feature Selection, Extraction and Dimensionality Reduction [Call for Papers] in IEEE Congress on Evolutionary Computation (CEC2019)
Special session on Evolutionary Computation for Feature Selection, Extraction and Dimensionality Reduction [Call for Papers] in IEEE Congress on Evolutionary Computation (WCCI 2018 /CEC2018)
IEEE Symposium on Computational Intelligence in Feature Analysis, Selection, and Learning in Image and Pattern Recognition (FASLIP) [Call for Papers] in IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2018)
IEEE Symposium on Computational Intelligence in Feature Analysis, Selection, and Learning in Image and Pattern Recognition (FASLIP) in IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017)
Special session on Evolutionary Computation for Feature Selection, Extraction and Dimensionality Reduction in The 11th International Conference on Simulated Evolution and Learning 2017 (SEAL 2017)
Special session on Evolutionary Computation for Feature Selection, Extraction and Dimensionality Reduction in IEEE Congress on Evolutionary Computation (CEC2017)
Special Issue on Evolutionary Optimisation, Feature Reduction and Learning, Applied Soft Computing (Journal), Springer, 2017
Special Session Chair in The 20th Asia-Pacific Symposium on Intelligent and Evolutionary Systems (IES2016), Canberra, Australia, November 16-18, 2016
Special session on Evolutionary Feature Selection and Construction in IEEE Congress on Evolutionary Computation (WCCI 2016 /CEC2016)
Special session on Evolutionary Feature Selection and Construction in IEEE Congress on Evolutionary Computation (CEC 2015)
Special session on Evolutionary Feature Reduction in The Tenth International Conference on Simulated Evolution And Learning (SEAL 2014)

People

Chair

Bach Hoai Nguyen, Victoria University of Wellington, New Zealand

Vice-Chairs

Bing Xue, Victoria University of Wellington, New Zealand
Yaochu Jin, Westlake University, China

Members

Kay Chen Tan, National University of Singapore, Singapore
Yew-Soon Ong, Nanyang Technological University, Singapore
Hisao Ishibuchi, Tohoku University, Japan
Carlos A. Coello Coello, Cinvestav-IPN, Mexico
Xiaodong Li, RMIT University, Australia
Mengjie Zhang, Victoria University of Wellington, New Zealand
Krzysztof Krawiec, Poznan University of Technology, Poland
Brijesh Verma, Central Queensland University, Australia
Stefano Cagnoni, Universita degli Studi di Parma, Italy
Stewart W. Wilson, Prediction Dynamics, USA
Zexuan Zhu, Shenzhen University, China
Kai Qin, RMIT University, Australia
Kourosh Neshatian, University of Canterbury, New Zealand
Andy Song, RMIT University, Australia
Ivy Liu, Victoria University of Wellington, New Zealand
Lin Shang, Nanjing University, China
Yi Mei, Victoria University of Wellington, New Zealand
Zhongyi Hu, Wuhan University, China
Emrah Hancer, Department of Computer Engineering, Erciyes University, Turkey
Harith Al-Sahaf, Victoria University of Wellington, New Zealand
Qi Chen, Victoria University of Wellington, New Zealand
Urvesh Bhowan, Amazon AWS
Liam Cervante, Google, United Kingdom
Ying Bi, Victoria University of Wellington, New Zealand
Qurrat Ul Ain, Victoria University of Wellington, New Zealand
Peng Wang, Victoria University of Wellington, New Zealand
Ruwang Jiao, Victoria University of Wellington, New Zealand