Journal of Universal Computer Science (J.UCS)

Special Issue Call for Papers

The Journal of Universal Computer Science welcomes contributions for a special issue:

HEMML 2012 (call for papers in pdf)

Hybrid and Ensemble Methods in Machine Learning

Guest editors

Contact

Przemysław Kazienko, Wroclaw University of Technology, Polandkazienko@pwr.wroc.pl
Edwin Lughofer, Johannes Kepler University Linz, Austriaedwin.lughofer@jku.at
Bogdan Trawiński, Wroclaw University of Technology, Poland bogdan.trawinski@pwr.wroc.pl

Objectives and topics

Hybrid and ensemble methods in machine learning have gained a great attention of scientific community over the last several years. Multiple learning models have been theoretically and empirically shown to provide significantly better performance than their single base models. Ensemble algorithms and hybrid methods of reasoning have found their application in various real word problems ranging from person recognition through medical diagnosis and text classification to financial forecasting. The HEMML 2012 Special Issue of Journal of Universal Computer Science http://www.jucs.org, is devoted to both hybrid and ensemble methods and their application to classification, prediction, and clustering problems. The impact factor of J.UCS is 0.669, the 5-year impact factor 0.788 (2010). All issues and papers are assigned a digital object identifier (DOI). After the success of the 4th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2012) and especially, the Special Session on Multiple Model Approach to Machine Learning (MMAML 2012) at this, we want to offer another opportunity for researchers and practitioners to extend their work and publish recent advances in this area. The scope of the special issue includes the following topics:
  • Theoretical framework for ensemble methods
  • Ensemble learning algorithms: bagging, boosting, stacking, etc.
  • Ensemble methods in clustering
  • Dealing with large volumes of data and lack of adequate data
  • Subsampling and feature selection in multiple model machine learning
  • Diversity, accuracy, interpretability, and stability issues
  • Homogeneous and heterogeneous ensembles
  • Hybrid methods in prediction and classification
  • Ensemble methods for dealing with concept drift
  • Incremental, evolving, and online ensemble learning
  • Mining data streams using ensemble methods
  • Multi-objective ensemble learning
  • Ensemble methods in agent and multi-agent systems
  • Implementations of ensemble learning algorithms
  • Assessment and statistical analysis of ensemble models
  • Applications of ensemble methods in business, engineering, medicine, etc.

International Reviewer Board (tentative)

  • Ethem Alpaydin, Bogaziçi University, Turkey
  • Abdelhamid Bouchachia, University of Klagenfurt, Austria
  • Rung-Ching Chen, Chaoyang University of Technology, Taiwan
  • Suphamit Chittayasothorn, King Mongkut's Institute of Technology Ladkrabang, Thailand
  • Oscar Cordón, University of Granada, Spain
  • José Alfredo F. Costa, Federal University (UFRN), Brazil
  • Ireneusz Czarnowski, Gdynia Maritime University, Poland
  • Fernando Gomide, State University of Campinas, Brazil
  • Lawrence O. Hall, University of South Florida, USA
  • Francisco Herrera, University of Granada, Spain
  • Tzung-Pei Hong, National University of Kaohsiung, Taiwan
  • Przemysław Kazienko, Wrocław University of Technology, Poland
  • Mark Last, Ben-Gurion University of the Negev, Israel
  • Chunshien Li, National Central University, Taiwan
  • Kun Chang Lee, Sungkyunkwan University, Korea
  • Edwin Lughofer, Johannes Kepler University Linz, Austria
  • Bogdan Trawiński, Wrocław University of Technology, Poland
  • Olgierd Unold, Wrocław University of Technology, Poland
  • Michał Woźniak, Wrocław University of Technology, Poland
  • Faisal Zaman, Kyushu Institute of Technology, Japan
  • Zhongwei Zhang, University of Southern Queensland, Australia
  • Zhi-Hua Zhou, Nanjing University, China
  • Indre Zliobaite, Bournemouth University, UK

Important dates

Expression of interest, submission of the tentative title via EasyChair: April 30, 2012
Submission of the paper for revision via EasyChair: June 30, 2012
Notification of paper acceptance: October 30, 2012
Revised version submission deadline: November 30, 2012
Camera-ready copies of accepted papers due: December 31, 2012

Submission

The submission of the title is required to form the list of papers and should be send to the EasyChair service at https://www.easychair.org/conferences/?conf=hemml2012; select “New Submission” , mark “Abstract Only” at the bottom. Since the abstract is not necessary that time, you may put any text in the Abstract field.

Since the special issue is, in a sense, a continuation of the ACIIDS 2012 conference and special session MMAML 2012 in particular, the authors are requested to significantly extend their achievements. The submission must contain at least 30-50% new material and the title of the extended version must clearly and unmistakably differ from the title of the article presented at the conference.

The submission of the paper for the revision should be send in the electronic version (PDF) via EasyChair available at https://www.easychair.org/conferences/?conf=hemml2012. To be fully considered for publication, papers must be received by the due date and meet the following requirements. Papers must be written in English and the maximal length of the final version should be 20 pages (incl. figures and tables) in the journal format. The electronic data of the final version of papers must be prepared in LaTeX according to the J.UCS guidelines.

All papers submitted will be reviewed by the independent reviewers. However, please note that this invitation does not mean your paper will be automatically accepted for publication.