CFP: Special Issue on Evolutionary Algorithms for Data Mining

Call for Papers: Special Issue on Evolutionary Algorithms for Data Mining

Journal: Genetic Programming and Evolvable Machines (pub. by Springer)

Guest Editors: Pierre Collet, Université de Strasbourg, France; Man Leung Wong, Lingnan University, Hong Kong

Editor-in-Chief: Lee Spector, Hampshire College

The corporate and scientific communities are overwhelmed with an influx of data that is stored in on-line databases. Analyzing these data and extracting meaningful information in a timely fashion is intractable without computer assistance and powerful analytical tools. Data Mining is defined as the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data stored in databases (Fayyad et al. 1996). It is an active research area with promise for high payoffs in many business and scientific applications including direct marketing, trend analysis, fraud detection, stock price forecasting, option trading, bond rating, portfolio management, shopping patterns analysis, and medical diagnosis (Simoudis et al. 1996, Groth 1998).

The future of scientific computing is parallel and Artificial Evolution is inherently parallel. Therefore, all the recent successes in Data Mining obtained using Genetic Programming, Genetic Algorithms, Evolutionary Programming, Evolution Strategies, etc. bear a great potential. The grand challenge of using Evolutionary Algorithms for Data Mining is to automatically process large quantities of raw incomplete data with noisy information, identify the most significant and meaningful information/knowledge and present it for achieving the user's goals.

The aim of this special issue is to provide authors with a possibility to publish their work in a renowned journal and the reader with an understanding of the grand challenge, novel approaches in tackling the grand challenge, and some real-life applications, be they already parallelized or not.

We encourage submission of high quality papers (original work that has neither appeared in, nor is under consideration by, other journals), both theoretical and empirical, discussing novel Evolutionary Algorithms for Data Mining. Practical papers that describe successful applications of Evolutionary Algorithms for challenging real-life data mining problems are also sought. Subjects will include (but are not limited to):

- Parallel evolutionary algorithms for data mining

- Data mining from incomplete, imprecise, noisy, imbalanced, and huge databases

- Evolutionary algorithms for cost-sensitive data mining

- Evolutionary ensemble techniques for data mining

- Evolutionary algorithms for supervised and unsupervised classification of data

We encourage all prospective authors to contact the guest editors, at the address below, as early as possible, to indicate your intention to submit a paper to this special issue.

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by, other journals.

Manuscripts should be submitted to: http://GENP.edmgr.com. Choose "Evol. Algorithms for Data Mining" as the article type when submitting.

Springer offers authors, editors and reviewers of Genetic Programming and Evolvable Machines a web-enabled online manuscript submission and review system. Our online system offers authors the ability to track the review process of their manuscript with straightforward log-in and submission procedures, and it supports a wide range of submission file formats.

== Guest Editors:

Pierre Collet, Université de Strasbourg, France; pierre.collet@unistra.fr

Man Leung Wong, Lingnan University, Hong Kong. mlwong@ln.edu.hk

== Important Dates:

Paper submission deadline: Dec 15, 2010

First Notification: March 15, 2011

Final manuscript: April 30, 2011

== References:

Fayyad, U. M., Piatetsky-Shapiro, G., and Smyth, P. (1996). From Data Mining to Knowledge Discovery: An Overview. In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (eds.), Advances in Knowledge Discovery in Data Mining, pp. 1-34. Menlo Park, CA: AAAI Press.

Groth, R. (1998). Data Mining: A Hands-On Approach for Business Professionals. Upper Saddle River NJ: Prentice Hall.

Simoudis, E., Han, J., and Fayyd, U. (1996). Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. Menlo Park CA: AAAI Press.