MMAML 2016 (call for papers in pdf)

Special Session on Multiple Model Approach to Machine Learning

at the 8th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2016)

Da Nang, Vietnam, March 14-16, 2016

Conference website: www.aciids.pwr.edu.pl

MMAML 2016 website: http://ksi.pwr.edu.pl/events/mmaml2016

Objectives and topics

Ensemble methods have gained great attention of scientific community over the last several years. Multiple models have been theoretically and empirically shown to provide significantly better performance than their single base models. Ensemble algorithms have found their application in various real word problems ranging from person recognition through medical diagnosis and text classification to financial forecasting. The MMAML 2016 Special Session at the 8th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2016) is devoted to the ensemble methods addressing classification, prediction, and clustering problems and their application to Big Data and small data sets as well as data streams and stationary data sets. We want to offer an opportunity for researchers and practitioners to identify new promising research directions as well as to publish recent advances in this area. The scope of the MMAML 2016 includes, but is not limited to the following topics:

  • Theoretical framework for ensemble methods
  • Ensemble learning algorithms: bagging, boosting, stacking, etc.
  • Ensemble methods in clustering
  • Dealing with Big Data and small data sets
  • 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
  • Incremental, evolving, and online ensemble learning
  • Mining data streams using ensemble methods
  • Ensemble methods for dealing with concept drift
  • 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.

Session chairs

Contact

Tomasz Kajdanowicz, Wroclaw University of Technology, Poland
Edwin Lughofer, Johannes Kepler University Linz, Austria
Bogdan Trawiński, Wroclaw University of Technology, Poland

tomasz.kajdanowicz@pwr.edu.pl
edwin.lughofer@jku.at
bogdan.trawinski@pwr.edu.pl

Important dates

Submission of papers: 1 October 2015
Notification of acceptance:
15 November 2015
Camera-ready papers:
15 December 2015
Registration&payment:
15 December 2015
Conference date:
14-16 March 2016

International Program Committee (to be invited)




Emili Balaguer-Ballester, Bournemouth University, UK

Urszula Boryczka, University of Silesia, Poland

Abdelhamid Bouchachia, Bournemouth University, UK

Robert Burduk, Wrocław University of Technology, Poland

Oscar Castillo, Tijuana Institute of Technology, Mexico

Rung-Ching Chen, Chaoyang University of Technology, Taiwan

Suphamit Chittayasothorn, King Mongkut's Institute of Technology Ladkrabang, Thailand

José Alfredo F. Costa, Federal University (UFRN), Brazil

Bogusław Cyganek, AGH University of Science and Technology, Poland

Ireneusz Czarnowski, Gdynia Maritime University, Poland

Patrick Gallinari, Pierre et Marie Curie University, France

Fernando Gomide, State University of Campinas, Brazil

Francisco Herrera, University of Granada, Spain

Tzung-Pei Hong, National University of Kaohsiung, Taiwan

Konrad Jackowski, Wrocław University of Technology, Poland

Piotr Jędrzejowicz, Gdynia Maritime University, Poland

Tomasz Kajdanowicz, Wrocław University of Technology, Poland

Yong Seog Kim, Utah State University, USA

Bartosz Krawczyk, Wrocław University of Technology, Poland

Kun Chang Lee, Sungkyunkwan University, Korea

Edwin Lughofer, Johannes Kepler University Linz, Austria

Hector Quintian, University of Salamanca , Spain

Andrzej Sieminski, Wroclaw University of Technology, Poland

Dragan Simic, University of Novi Sad, Serbia

Adam Słowik, Koszalin University of Technology, Poland

Zbigniew Telec, Wrocław University of Technology, Poland

Bogdan Trawiński, Wrocław University of Technology, Poland

Krzysztof Trawiński, European Centre for Soft Computing, Spain

Olgierd Unold, Wrocław University of Technology, Poland

Pandian Vasant, University Technology Petronas, Malaysia

Michał Woźniak, Wrocław University of Technology, Poland

Zhongwei Zhang, University of Southern Queensland, Australia

Zhi-Hua Zhou, Nanjing University, China

Submission

All contributions should be original and not published elsewhere or intended to be published during the review period. Authors are invited to submit their papers electronically in pdf format, through EasyChair. All the special sessions are centralized as tracks in the same conference management system as the regular papers. Therefore, to submit a paper please activate the following link and select the track: MMAML 2016: Special Session on Multiple Model Approach to Machine Learning.

https://www.easychair.org/conferences/?conf=aciids2016

Authors are invited to submit original previously unpublished research papers written in English, of up to 10 pages, strictly following the LNCS/LNAI format guidelines. Authors can download the Latex (recommended) or Word templates available at Springer's web site. Submissions not following the format guidelines will be rejected without review. To ensure high quality, all papers will be thoroughly reviewed by the MMAML 2016 International Program Committee. All accepted papers must be presented by one of the authors who must register for the conference and pay the fee. The conference proceedings will be published by Springer in the prestigious series LNCS/LNAI (indexed by ISI CPCI-S, included in ISI Web of Science, EI, ACM Digital Library, dblp, Google Scholar, Scopus, etc.).

Post-conference publication

A selected number of accepted and personally presented papers will be expanded and revised for possible inclusion in special issues in high quality scientific journals. So far, the best papers presented during MMAML special sessions have been invited to special issues on Hybrid and Ensemble Methods in Machine Learning of the following prestigious journals: New Generation Computing (IF=0.941) published in 2011, International Journal of Applied Mathematics and Computer Science (IF=0.487) published in 2012, Journal of Universal Computer Science (IF=0.669) published in 2013, and journal Soft Computing (IF=1.124), to be published in Fall 2014. At present, we are inviting papers to the Special Issue on Hybrid Ensemble Machine Learning for Complex and Dynamic Data in New Generation Computing (IF=0.941), to be published in 2016. Call for papers you can find under the address: http://kms.ii.pwr.wroc.pl/events/HEMLCDD2014