The 14th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL'2013)

Special Sessions

We are happy to announce that the following special sessions have been approved for IDEAL'13. (Detailed information for each session is given in the corresponding, linked websites.)

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 http://nical.ustc.edu.cn/ideal13/ss_alms.html.

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 http://web.utk.edu/~wzhou4/ideal13bigdata.htm.

Special Session on Soft-Computing Algorithms in Renewable Energy Problems

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 welcome.

For more information, see the special session web site http://nical.ustc.edu.cn/ideal13/ss_scarep.html.

Special Session on Swarm Intelligence and Data Mining (SDIM 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 http://sidm2013.nclab.tw (or under http://nical.ustc.edu.cn/ideal13/ss_sidm.html).

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:

  • 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?
  • how we can circumvent the barrier of lacking enough annotated data, despite the vast quantities of unannotated data?
  • how we can adapt machine learning solutions across domains, genres, and languages?
  • how we can make full use of the characteristics of text data in building machine learning based solutions?
  • 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 http://www.scss.tcd.ie/IDEAL2013-TDL/ and the pdf CfP.

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 http://baggins.nottingham.edu.my/~khczcsy/ideal2013coevo.html. and the pdf CfP.

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 http://www.cs.bham.ac.uk/~axk/ss_IDEAL13_Opt+Learning.htm.