In data mining and machine learning, many real-world problems involve a large number of features, which leads to the problem known as "the curse of dimensionality". However, not all the features are essential since many of them are redundant or even 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 due to the large search space and feature interaction problems. Recently, there has been increasing interest in using evolutionary computation techniques to solve feature reduction problems.
The theme of this special session is the use of evolutionary computation for feature reduction, covering ALL different evolutionary computation paradigms, including Genetic algorithms (GAs), Genetic programming (GP), Evolutionary programming (EP), Evolution strategies (ES), Learning classifier systems (LCS), Particle swarm optimization (PSO), Ant colony optimization (ACO), Differential evolution (DE), Artificial immune systems (AIS), Evolutionary Multi-objective optimization (EMO), Estimation of distribution algorithms (EDA), Cultural algorithms (CA).
The aim is to investigate both the new theories and methods in different evolutionary computation paradigms to feature reduction, and the applications of evolutionary computation for feature reduction. Authors are invited to submit their original and unpublished work to this special session. Topics of interest include but are not limited to:
- Feature ranking/weighting
- Feature subset ranking
- Feature subset selection
- Filter, wrapper, and embedded methods for feature selection
- Multi-objective feature selection
- Feature construction/extraction
- Single feature or multiple features construction
- Filter, wrapper, and embedded methods for feature construction
- Multi-objective feature construction
- Analysis on evolutionary feature selection and construction algorithms
- Feature selection and construction in classification, clustering, regression, and other tasks
- Real-world applications of evolutionary feature selection and construction, e.g. gene analysis, biomarker detection, medical data classification, diagnosis, and analysis, image analysis, face recognition, hand written digit recognition, text mining, network intrusion detection, power system, financial and business data analysis, et al.
- Paper Submission: 19 December 2014
- Decision Notification: 20 February 2015
- Camera-Ready Submission: 13 March 2015
Paper Submission:Please follow the IEEE CEC 2015 Submission Web Site. Special session papers are treated the same as regular conference papers. All papers accepted and presented at CEC 2015 will be included in the conference proceedings published by IEEE Explore, which are typically indexed by EI.
School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand. Phone: +64-4-463 5233+8874; Fax: +64-4-463 5045. (Homepage)
School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand. Phone: +64-4-463 5654; Fax: +64-4-463 5045. (Homepage)