The following positions within the IEEE Computational Intelligence Society (CIS) become vacant on 1/1/2017:

• • President Elect (2017)
• • VP Publications (2017-18)
• • VP Member Activities (2017-18)
• • VP Education (2017-18)
• • Five ADCOM Members-at-Large (2017-2019)

According to the CIS Bylaws, "Section 31. Schedule for ADCOM Elections: Five ADCOM Members-at-Large are elected each year, plus any vacated positions. The election of President-Elect, Vice President for Education, Vice President for Member Activities, and Vice President for Publications shall take place in even-numbered years."

This is the official call for nominations, including self-nominations, for the above vacancies. The nomination materials should include the following:

• (1) A brief CV of no more than 2 pages.
• (2) A position statement of no more than 2 pages, explaining (a) your vision of CIS's publication / member activities / education activities (depending on the position) and how they contribute to CIS's overall growth and serve members' needs; (b) challenges faced by the CIS's publication / member activities / education activities and your ideas of tackling them; (c) the objectives (has to be SMART) of your term (if elected) and beyond.
• (3) A list of previous volunteering experience within CIS, IEEE, and other professional organisations, and relevant achievements. (No more than 2 pages)

All the nominations and self nominations should be sent to the Chair of the CIS Nominations Committee, Prof. Xin Yao ([email protected]) in this case, and copy to Jo-Ellen Snyder ([email protected]) by 30/06/2016 (Thursday).

Please check CIS Bylaws for the nominee's eligibilty for different positions: http://cis.ieee.org/bylaws.html.

Xin Yao
Chair of CIS Nominations Committee (2016)
CIS Past President (2016)

As a technology-driven society, CIS leads and promotes the use of the technologies. From 1 January 2015, all complimentary copies of the IEEE Computational Intelligence Magazine (CIM) to our members will be delivered electronically only. However, paper copies are still available, for a small fee ($15.00), to those who need them. As a society, we are making our small contributions to the environment by cutting the use of paper.

On behalf of the organizing committee, it is our great pleasure to invite you to the bi-annual IEEE World Congress on Computational Intelligence (IEEE WCCI) which will be held in the magnificent city of Vancouver, Canada, 24-29 July 2016. Financially sponsored by the IEEE Computational Intelligence Society (CIS), IEEE WCCI 2016 will host three conferences: The 2016 International Joint Conference on Neural Networks (IJCNN 2016), the 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016), and the 2016 IEEE Congress on Evolutionary Computation (IEEE CEC 2016) under one roof.

The IEEE WCCI 2016 received over 3,000 paper submissions (a record for the Congress), and the highlights of the Congress include:

• • 5 Keynotes by top-notch researchers - Leslie Valiant, Ronald R. Yager, Marco Dorigo, Ricardo Baeza-Yates and Charles C. Ragin;
• • 15 Plenary speeches by world-renown scholars on pertinent and captivating topics within the technical scope of each conference;
• • A public lecture by David B. Fogel on the topic of “Fun and Games with Artificial Intelligence”;
• • A few valuable and engaging panel sessions by revered researchers within the field of computational intelligence;
• • 24 tutorials covering a diverse range of topics offering a unique opportunity to disseminate in-depth information on specific topics in computational intelligence;
• • A number of workshops to stimulate discussions between participants on active and emerging topics of computational intelligence;
• • 175 special sessions organized by domain experts that are encompassed within the technical scope of the conference;
• • 9 challenging and contemporary competitions offering distinctive topics in each track to cater to every participant’s needs.

The IEEE WCCI 2016 will be held at the Vancouver Convention Centre, Vancouver, Canada. Vancouver is Canada’s Pacific gem, offering a winning combination of world-class hotels, meeting venues, and restaurants in a setting of spectacular beauty. Few convention cities can offer such a wide range of cosmopolitan amenities in a downtown core that is safe, clean, pedestrian friendly, and stunning in its backdrop of mountains and ocean.

Apart from the technical programs, participants are also cordially invited to attend various social events that will include welcome reception and award banquet. In addition, participants are also encouraged to explore the beautiful city of Vancouver which has an endless supply of attractions and things to see and do.

Observer-Biased Fuzzy Clustering, by P. Fazendeiro and J. V. de Oliveira, IEEE Transactions on Fuzzy Systems, 23(1):85-97, 2015.

DOI: 10.1109/TFUZZ.2014.2306434
URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6740819

As generated by clustering algorithms, clusterings (or partitions) are hypotheses on data explanation which are better evaluated by experts from the application domain. In general, clustering algorithms allow a limited usage of domain knowledge about the cluster formation process. In this study, we propose both a design technique and a new partitioning-based clustering algorithm which can be used to assist the data analyst while looking for a set of meaningful clusters, i.e., clusters that actually correspond to the underlying data structure. Following an observer metaphor according to which the perception of a group of objects depends on the observer position—the closer an observer is from an image more details (s)he perceives—we resort to shrinkage to incorporate a regularization term, accounting for the observation point, within the objective function of an otherwise unbiased clustering algorithm. This technique allows our resulting biased algorithm to generate a set of reasonable partitions, i.e., partitions validated by a given cluster validity index, corresponding to views of data with different levels of granularity (levels of detail) in different regions of the data space. For the illustration of the design technique, we adopted the fuzzy c-means (FCM) algorithm as the unbiased clustering algorithm and include a convergence theorem assuring that changing the point of observation in the corresponding biased algorithm FCM with focal point (FCMFP) does not jeopardize its convergence. Experimental studies on both synthetic and real data are included to illustrate the usefulness of the approach. In addition, and as a convenient side effect of using shrinkage, the experimental results suggest that our biased algorithm (FCMFP) not only seems to scale better than the successive runs of the unbiased one (FCM) but on the average, seems to produce clusters exhibiting higher validity index values as well. In addition, less sensitivity to initialization was observed for the biased algorithm when compared with the unbiased one.