IEEE CIS Task Force on
Evolutionary Computation for Feature Selection and Construction
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.
Events
Coming Events:
Past Events:
- 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)
Chair
Victoria University of Wellington, New Zealand, Bing.Xue@ecs.vuw.ac.nz
Vice Chairs
University of Surrey, United Kingdom,
yaochu.jin@surrey.ac.uk
Victoria University of Wellington, New Zealand,
Mengjie.Zhang@ecs.vuw.ac.nz
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
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, Kayseri 38039, Turkey
Harith Al-Sahaf, Victoria University of Wellington, New Zealand
Qi Chen, Victoria University of Wellington, New Zealand
Urvesh Bhowan, IBM, Ireland
Liam Cervante, Google, United Kingdom