SPECIALSESSIONSEVOLUTIONARY COMPUTATION FORFEATURE SELECTION, EXTRACTION AND
DIMENSIONALITY REDUCTION
Bing Xue, Mengjie Zhang and Yaochu Jin

In machine learning and data mining, the quality of the input data determines the qualityof the output (e.g. accuracy), known as the GIGO (Garbage In, Garbage Out) principle. For agiven problem, the input data of a learning algorithm is almost always expressed by anumber of features (attributes or variables). Therefore, the quality of the feature space is akey for success of any machine learning and data algorithm.
Feature selection, feature extraction or construction and dimensionality reduction areimportant 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 ofimportant (relevant) features from the original full feature set. Feature extraction orconstruction aims to extract or create a set of effective features from the raw data or create asmall number of (more effective) high-level features from (a large number of) low-levelfeatures. Dimensionality reduction aims to reduce the dimensionality of the data space withthe focus of solving “the curse of dimensionality” issue. All of them can potentially improve
the performance of a learning algorithm significantly in terms of the accuracy, increase thelearning speed, and the complexity and the interpretability of the learnt models. However,they are challenging tasks due to the large search space and feature interaction problems.
Recently, there has been increasing interest in using evolutionary computation techniques tosolve these tasks due to the fast development of evolutionary computation and capability ofstochastic search, constraint handling and dealing with multiple conflict objectives.
The theme of this special session is the use of evolutionary computation for featurereduction, covering ALL different evolutionary computation paradigms. The aim is toinvestigate both the new theories and methods in different evolutionary computation
paradigms to feature selection, feature extraction and construction, dimensionality reduction
and related studies on improving quality of the feature space, and their applications. Authors
are invited to submit their original and unpublished work to this special session.

Special Session Chair
Ke Tang · ketang@ustc.edu.chnIEEE Conference on Evolutionary Computation
Donostia – San Sebastián, June 8-5
www.cec2017.org · info@cec2017.org

SPECIALSESSIONSTopics•

Dimensionality reduction•

Feature ranking/weighting•

Feature subset selection•

Multi-objective feature selection•

Filter, wrapper, and embedded methods for feature selection•

Feature extraction or construction•

Single feature or multiple features construction•

Filter, wrapper, and embedded methods for feature extraction•

Multi-objective feature extraction•

Feature selection, extraction, and dimensionality reduction in image analysis, patternrecognition, classification, clustering, regression, and other tasks•

Feature selection, extraction, and dimensionality reduction on high-dimensional andlarge-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, etc.

2/2