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IDEAL 2013 : The International Conference on Intelligent Data Engineering and Automated Learning


Conference Series : Intelligent Data Engineering and Automated Learning
When Oct 20, 2013 - Oct 23, 2013
Where Hefei, Anhui, China
Submission Deadline Jun 10, 2013
Notification Due Jul 5, 2013
Final Version Due Jul 26, 2013
Categories    machine learning   information management   bio-inspired computation   big data

Call For Papers

======= Call for Papers: IDEAL'13, October 2013, Hefei, China =======

The 14th International Conference on Intelligent Data Engineering and
Automated Learning (IDEAL'2013)
October 20-23, 2013, Hefei, Anhui, China

The International Conference on Intelligent Data Engineering and
Automated Learning (IDEAL) is an annual international conference
dedicated to emerging and challenging topics in intelligent data
analysis, data mining and their associated learning systems and
paradigms. Its core themes include: Big Data challenges, Machine
Learning, Data Mining, Information Retrieval and Management, Bio- and
Neuro-Informatics, Bio-Inspired Models (including Neural Networks,
Evolutionary Computation and Swarm Intelligence), Agents and Hybrid
Intelligent Systems, and Real-world Applications of Intelligent
Techniques. Other related and emerging themes and topics are also

The conference provides a unique opportunity and stimulating forum for
presenting and discussing the latest theoretical advances and real-
world applications in Computational Intelligence and Intelligent Data
Analysis. It also features a panel discussion on Big Data chaired by
Prof. Zhi-Hua Zhou. Authors and researchers are warmly invited to
submit their latest findings and research work to the conference.

A number of leader experts in the field will give plenary speeches at
the conference. More details can be found or will appear on the
conference website

Instructions for Authors

Authors are invited to submit their manuscripts (in pdf format)
written in English via the conference online submission system
( All submissions
will be refereed by experts in the field based on originality,
significance, quality and clarity. All contributions must be original,
must not have been published elsewhere and must not be submitted
elsewhere during the review period. Papers should not exceed 8 pages
and must comply with the format of Springer LNCS/LNAI Proceedings.

Accepted papers presented at the conference will be included in the
Proceedings of IDEAL 2013, to be published by Springer in its LNCS
series, which is indexed in EI. In addition, selected papers will be
invited for special issues in the folloing leading international
journals in the field:

- International Journal of Neural Systems
- Connection Science

Important Dates:

Paper Submission Deadline: 10 June 2013 (extended)
Notification of Acceptance: 05 July 2013
Camera-Ready Copy Due: 26 July 2013
Early Registration: 26 July 2013
Conference Presentation: 20-23 October 2013
Conference Website:

Conference History

In recent years, the IDEAL conference has been held in many countries
or continents such as Brazil (2012), England (2011), Scotland (2010),
Spain (2009), and Korea (2008). The 14th International Conference,
IDEAL 2014, will set foot to Mainland China and will be held from 20th
to 23rd October 2013, in Hefei, China, hosted by the USTC-Birmingham
Joint Research Institute of Intelligent Computation and Its
Applications (UBRI,


IDEAL'13 will be held at the Empark Grand Hotel, Anhui in Hefei, China.
Hefei, the capital of the Anhui Province, is a fine historical city
characterized by a green environment and both modern areas and
historical sights. The city is located centrally in China and about
100 miles (160 km) from Nanjing, or 300 miles (500 km) from Shanghai.
Hefei has its own airport and an excellent railway connection to many
cities of China. It is also easy to reach via airplane from Beijing.
Hefei, well known as a historic site famous from the Three Kingdoms
Period and the home town of Lord Bao, is a city with a history of more
than 2500 years. The city of Hefei is also a well-known "Green City"
across the nation. It is a fast developing city which still preserves
historical sights and has many local attractions.

========================== Special Sessions ==========================

We are happy to announce that the following special sessions have been
approved for IDEAL'13:

Special Session on Adaptive and Learning Multi-Agent Systems

Multi-Agent Systems (MAS) have grown into an interdisciplinary field
that includes various tracks and embraces many previously
distinctive research areas. More and more MASs are situated in open
and dynamic environments. The changes of environments that may be
unpredictable, uncontrollable and evolving typically affect the MAS.
Recently, adaptive MAS and MAS learning have become important sub-
areas in the literature of MAS. Particularly, both of them
investigate how multiple intelligent computational agents can work
together to achieve high- level goals by adjusting themselves and
obtaining more information. Various approaches have been applied to
improve the adaptive and learning ability of MAS. MAS are still
facing challenges of scaling to large numbers of entities and real-
world tasks.

This special session on adaptive and learning multi-agent systems
will provide a forum for researchers and practitioners interested in
adaptation and learning for multi-agent systems, and report their
latest findings.

For more information, see the special session web site

Organizers: Dong, Hongbin. Harbin Engineering University, China
He, Jun. Aberystwyth University, UK
Mao, Xinjun. National University of Defense Tech., China
Tong, Xiangrong. Yantai University, China

Special Session on Big Data

Recent years have witnessed the unprecedented prevalence of "Big
Data". Big Data is transforming science, engineering, medicine,
healthcare, finance, business, and ultimately, the society itself.
This year IDEAL'2013 is pleased to introduce a Special Session on
Big Data. We wish to encourage researcher to submit high-quality
original papers (including significant work-in-progress) in any
aspect of Big Data with emphasis on 5Vs (Volume, Velocity, Variety,
Value and Veracity): big data science and foundations, big data
infrastructure, big data management, big data searching and mining,
big data privacy/security, and big data applications.

For more information, see the special session web site

Oranizers: Hui Xiong. Rutgers University, USA
Wenjun Zhou. University of Tennessee, USA

Special Session on Soft-Computing Algorithms in Renewable Energy

In the current context of world economic crisis, Renewable Energies
are of crucial importance towards a cleaner and more sustainable
future. Several factors have recently pushed Renewable Energies,
such as recent proofs of the direct connection between global
warming and CO2 emissions from fossil fuels, the intended reduction
of greenhouse gasses thanks to the Kyoto protocol or the growing of
the risk perception after the nuclear accident in Japan in December
2011, among others.

Nevertheless, the establishment and maximum exploitation of
Renewable Energy still need a lot of work and research effort. Many
of the problems that arise in Renewable Energy are so difficult,
that traditional mathematical methods do not obtain good results.
The design of new renewable energy facilities (wind farms, solar
plants, smart and micro-grids with renewable generation, or stand-
alone systems, etc.), the correct estimation of the renewable energy
resource (wind, radiation, reservoir levels) or the optimization of
technologies to obtain more productive systems (wind turbine design,
solar panels design), are just some examples of these hard problems
related to renewable energy.

In these problems, the use of Soft-Computing approaches has been
massive in the last few years, as powerful computational methods
that obtain good results, with moderate computational effort. This
Special Session is focused on Soft-Computing approaches in Renewable
Energy problems, in a broad sense. We consider all Renewable Energy
technologies where Soft-Computing approaches can be used to improve
the final systems. Real problems and case studies are particularly

For more information, see the special session web site

Organizers: Sancho Salcedo Sanz. Universidad de Alcalá, Spain.
Jose Antonio Portilla-Figueras. Univ. de Alcalá, Spain.

Special Session on Swarm Intelligence and Data Mining (SIDM 2013)

Swarm intelligence is a recent trend in computational intelligence
and popular for the simplicity of its realizations, such as particle
swarm optimization (PSO), ant colony optimization (ACO), bee colony
optimization (BCO), and the like. As optimization techniques,
methods in swarm intelligence have been applied to many aspects in
the fields of data engineering and automated learning. For example,
as reported in the literature, PSO has been adopted to handle data
clustering, and ACO has been employed to solve the problem of
classification. On the other hand, advances in data mining, an
important section in data engineering and automated learning, also
assist optimization algorithm designers to develop better methods.
For instance, Apriori algorithm has been utilized for finding the
relationship among decision variables for optimizers. In order to
bridge the concepts and methodologies from the two ends, this
special session concentrates on the related topics of integrating
and utilizing algorithms in swarm intelligence and data mining. It
provides the opportunity for practitioners handling their data
mining issues by using swarm intelligence methodologies and for
researchers investigating swarm intelligence with data mining
approaches to share findings and look into future directions.

For more information, see the special session web site (or under

Organizers: Jing Liang, Zhengzhou University, China
Chuan-Kang Ting, National Chung Cheng Univ., Taiwan
Ying-ping Chen, National Chiao Tung Univ., Taiwan

Special Session on Text Data Learning

Tremendous efforts have been devoted to developing and applying
different machine learning technologies to natural language text
data, greatly expanding the fields of information retrieval and
natural language processing, creating new areas of research.
However, many challenges remain, such as:

o how we can successfully process different natural language
related tasks with machine learning: ranking documents,
classifying text, clustering, summarizing, analyzing, extracting
information, and so on?

o how we can circumvent the barrier of lacking enough annotated
data, despite the vast quantities of unannotated data?

o how we can adapt machine learning solutions across domains,
genres, and languages?

o how we can make full use of the characteristics of text data in
building machine learning based solutions?

o how we can create text learning systems to process Big Data in
distributed and parallel environments?

This special session within IDEAL2013 on text data learning will
provide a forum for researchers and practitioners interested in
information retrieval and natural language processing to exchange
and report their latest findings in applying machine learning to
understanding and mining natural language text data.

For more information, see the special session web site

Organizers: Baoli Li. Henan University of Technology, China
Carl Vogel. Trinity College Dublin, Ireland

Special Session on Coevolution

Bio-Inspired methodologies that are based on the natural
coevolutionary process have been applied successfully to solve a
variety of machine learning problems. In particular, competitive
coevolution is used to solve difficult adversarial problems such as
games whereby the target functions are unknown and that training
samples are unavailable for supervised learning methods. Competitive
coevolution seeks to solve these problems naturally with one
population consisting of candidate solutions (e.g. game strategies)
and another population consisting of test cases (e.g. test
strategies) that interact and undergo adaptation in a manner that
promotes the search for problem solutions while using typically a
small number of representative test cases that are discovered. Other
research studies have been made in the framework of cooperative
coevolution and its novel use to solve complex real-world learning
problems that are amenable to divide-and-conquer approaches.
Examples include ensemble learning for classification tasks and data
mining through Bayesian networks. Furthermore, recent theoretical
studies have been made for coevolutionary learning. These include
quantitative performance analysis of coevolutionary algorithms
through the generalization framework from machine learning, which
provide the means for in-depth analysis how specific designs of
components (e.g., selection and variation operators) can affect the
performance of coevolutionary learning. This special session aims to
bring together researchers in theoretical aspects and practitioners
in the real-world problem solving applications of coevolution.

For more information, see the special session web site

Organizers: Siang Yew Chong, University of Nottingham, Malaysia
Zhenyu Yang, National University of Defense Tech., China
Xiaodong Li, Royal Melbourne Inst. of Techn., Australia

Special Session on Combining Learning and Optimisation for Intelligent
Data Engineering

Techniques of Machine Learning and Optimisation are workhorses in
intelligent data engineering and in today's emerging data science.
Finding ways to combine learning with optimisation has tremendous
potential to provide powerful computational intelligence techniques.
In fact, optimisation is a key in many machine learning and data
mining algorithms; at the same time optimisation methods that
incorporate some form of learning strategy have an added level of
sophistication and ability to explore large search spaces.

This special session aims at exploring new synergies and multi-
disciplinary perspectives between optimisation and machine learning
in the context of intelligent data engineering and large scale data
mining problems.

For more information, see the special session web site

Organizer: Ata Kaban, The University of Birmingham, UK

================== Organizing Committee and Contact ==================


Programme Chair: Hujun Yin
School of Electrical and Electronic Engineering,
The University of Manchester,
Manchester, M13 9PL, UK.
Tel: +44 161 306 8714

Programme Co-Chair: Ke Tang
USTC-Birmingham Joint Research Institute of Intelligent Computation and
Its Applications (UBRI), School of Computer Science and Technology,
University of Science and Technology of China,
Hefei, Anhui, China, 230027
Tel: +86 551 3600 547

Conference Chairs and Organizers

o General Chair: Xin Yao (

o Programme Chair: Hujun Yin (

o Programme Co-Chairs:
- Ke Tang (
- Yang Gao (
- Frank Klawonn (
- Min-ho Lee (

o Publicity Co-Chairs:
- Emilio Corchado (
- Jose A. Costa (
- Thomas Weise (

o Organizing Committee:
- Bin Li (Chair) (
- Kaiming Chen (
- Jinlong Li (
- Thomas Weise (
- Rui Xu (

o International Liaisons:
- China/Visa: Jinlong Li (
- Europe: David Camacho (
- America: Guilherme Barreto (
- Australasia: Brijesh Verma (

o International Advisory Committee
- Lei Xu (Chair) - Yaser Abu-Mostafa
- Shun-ichi Amari - Michael Dempster
- Nick Jennings - Soo-Young Lee
- Erkki Oja - Latit M. Patnaik
- Burkhard Rost - Xin Yao

o Steering Committee
- Hujun Yin (Co-chair) - Laiwan Chan (Co-chair)
- Guilherme Barreto - Yiu-ming Cheung
- Emilio Corchado - Jose A. Costa
- Colin Fyfe - Marc van Hulle
- Samuel Kaski - John Keane
- Jimmy Lee - Malik Magdon-Ismail
- Vic Rayward-Smith - Peter Tino
- Zheng Rong Yang - Ning Zhong

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