IEEE Taskforce on Evolutionary Computation for Feature Selection and Construction

IEEE Computational Intelligence Society

Evolutionary Computation Technical Committee


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.



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.


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:

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:

Past Events and Activities