31 January-2 February 2017, Melbourne, Australia
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Tutorial: Evolutionary Feature Selection and Feature Construction for Dimensionality Reduction

In data mining and machine learning, many real-world problems such as bio-data classification and biomarker detection, image analysis, text mining often involve a large number of features/attributes. 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. Using all the features for classification or other data mining tasks typically does not produce good results due to the big dimensionality and the large search space. This problem can be solved by feature selection to select a small subset of original (relevant) features or feature construction to create a smaller set of high-level features using the original low-level features.

Feature selection and construction are very challenging tasks due to the large search space and feature interaction problems. Exhaustive search for the best feature subset of a given dataset is practically impossible in most situations. A variety of heuristic search techniques have been applied to feature selection and construction, but most of the existing methods still suffer from stagnation in local optima and/or high computational cost. Due to the global search potential and heuristic guidelines, evolutionary computation techniques such as genetic algorithms, genetic programming, particle swarm optimisation, ant colony optimisation, differential evolution and evolutionary multi-objective optimisation have been recently used for feature selection and construction for dimensionality reduction, and achieved great success. Many of these methods only select/construct a small number of important features, produce higher accuracy, and generated small models that are efficient on unseen data. Evolutionary computation techniques have now become an important means for handle big dimensionality and feature selection and construction.

The tutorial will introduce the general framework within which evolutionary feature selection and construction can be studied and applied, sketching a schematic taxonomy of the field and providing examples of successful real-world applications. The application areas to be covered will include bio-data classification and biomarker detection, image analysis and object recognition and pattern classification, symbolic regression, network security and intrusion detection, and text mining. EC techniques to be covered will include genetic algorithms, genetic programming, particle swarm optimisation, differential evolution, ant colony optimisation, artificial bee colony optimisation, and evolutionary multi-objective optimisation. We will show how such evolutionary computation techniques can be effectively applied to feature selection/construction and dimensionality reduction and provide promising results.

Targeted audience

This tutorial should be of interest to both new beginners and experienced researchers in the area of evolutionary feature selection and feature construction. The tutorial will provide a unique opportunity to showcase the latest development on this hot research topic to the research community. We expect the tutorial will last about 110 minutes.


Bing Xue
Victoria University of Wellington, New Zealand
Bing Xue is currently a lecturer and co-leader of the Evolutionary Computation Research Group, School of Engineering and Computer Science at Victoria University of Wellington, and leading the strategic research direction on evolutionary feature selection and construction. Her research focuses mainly on evolutionary computation, pattern recognition, feature selection, feature construction, multi-objective optimisation, data mining and machine learning.
She has over 70 papers published in fully referred international journals and conferences and most of them are on evolutionary feature selection and construction. She is currently co-supervising over 10 PhD and Master’s students and visiting scholars, and over 10 Honours and summer research projects.
Dr Xue is the main Chair of 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 2016), a program co-chair of the 7th International Conference on Soft Computing and Pattern Recognition (SoCPaR2015), Publicity Chair for the Australian Conference on Artificial Lift and Computational Intelligence (ACALCI 2017), Special Session co-Chair for The 20th Asia-Pacific Symposium on Intelligent and Evolutionary Systems (IES2016), Special Session Co-chair on Evolutionary Feature Reduction in the international conference on Simulated Evolution And Learning (SEAL 2014), and the main organiser of the special session on Evolutionary Feature Selection and Construction in IEEE Congress on Evolutionary Computation (CEC) 2015 and 2016. She is a member of Editorial Board for Applied Soft Computing (journal), International Journal of Computer Information Systems and Industrial Management Applications and International Journal of Swarm Intelligence Research, and also a Guest Editor for the Special Issue on Evolutionary Feature Reduction and Machine Learning for the Springer Journal of Soft Computing. Dr Xue is serving as a reviewer of over 10 international journals including IEEE Transactions on Evolutionary Computation, IEEE Transaction on Cybernetics and Information Sciences. She is a program committee member for many international conferences including Genetic and Evolutionary Computation Conference (GECCO), European Joint Conference on Evolutionary Computation (EvoStar -- EuroGP, EvoCOP and EvoApplications), IEEE Congress on Evolutionary Computation (CEC), International Joint Conference on Artificial Intelligence (IJCAI), Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), and International Conference on Simulated Evolution and Learning (SEAL).
Dr Xue is currently the Chair of the IEEE Task Force on Evolutionary Feature Selection and Construction, consisting of over 20 members for the five continents working in this area. She is also serving as the Director of Women in Engineering for the IEEE New Zealand Central Section and the Secretary of the IEEE Chapter on Computational Intelligence in that Section.
Mengjie Zhang, Victoria University of Wellington
Mengjie Zhang is currently Professor of Computer Science at Victoria University of Wellington, where he heads the interdisciplinary Evolutionary Computation Research Group. He is a member of the University Academic Board, a member of the University Postgraduate Scholarships Committee, a member of the Faculty of Graduate Research Board at the University, Associate Dean (Research and Innovation) in the Faculty of Engineering, and Chair of the Research Committee of the Faculty of Engineering and School of Engineering and Computer Science.
His research is mainly focused on evolutionary computation, particularly genetic programming, particle swarm optimisation and learning classifier systems with application areas of feature selection/construction and dimensionality reduction, computer vision and image processing, job shop scheduling, multi-objective optimisation, and classification with unbalanced and missing data. He is also interested in data mining, machine learning, and web information extraction. Prof Zhang has published over 400 research papers in refereed international journals and conferences in these areas.
He has been serving as an associated editor or editorial board member for seven international journals including IEEE Transactions on Evolutionary Computation, the Evolutionary Computation Journal (MIT Press), Genetic Programming and Evolvable Machines (Springer), Applied Soft Computing, IEEE Transactions on Emergent Topics in Computational Intelligence, Natural Computing, and Engineering Applications of Artificial Intelligence, and as a reviewer of over 30 international journals. He has been involving major EC conferences such as GECCO, IEEE CEC, EvoStar, IEEE SSCI and SEAL as a Chair. He has also been serving as a steering committee member and a program committee member for over 100 international conferences including all major conferences in evolutionary computation. Since 2007, he has been listed as one of the top ten world genetic programming researchers by the GP bibliography.
Prof Zhang is the Chair of the IEEE Emergent Technologies Technical Committee, the immediate Past Chair of the IEEE CIS Evolutionary Computation Technical Committee, a vice-chair of the IEEE CIS Task Force on Evolutionary Feature Selection and Construction, a vice-chair of the IEEE CIS Task Force on Evolutionary Computer Vision and Image Processing, and the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.

Important Dates
Paper submission deadline: 10 September 2016
Decision notification: 17 October 2016
Camera ready submission: 7 November 2016
Conference dates: 31 January-2 February 2017
Tutorials: 3 February 2017 (at RMIT University)

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