Connectionists: Call for papers: Data mining with meta-learning and hierarchical architectures

[Norbert Jankowski]

Call for papers: Special session: Data mining with meta-learning and
hierarchical architectures


The IEEE World Congress on Computational Intelligence, Beijing, China, 6-11
July 2014

Goals of this special session:

This session will be devoted to approaches that in an intelligent way
integrate various components of learning algorithms used for data mining,
especially in meta-learning, multimodel architectures, facilitating
knowledge transfer and deep learning. Integration of machine learning
algorithms becomes increasingly more important, especially in applications
to hard problems which still wait to be solved, where application of
specialized methods that do not use additional knowledge has led to limited
success. Hard problems and big data need much more than single neural
network or single learning machine. Sophisticated data transformations play
more and more important role. Data mining packages contain hundreds of
algorithms that may be composed in millions of ways. Automatization of this
process requires analysis of learning algorithms at the meta-level. Methods
that extract various forms of useful knowledge, share and integrate it for
intelligent information processing, are necessary to solve hard problems.
Such methods may be inspired by the organization of the brain, or may be
based on formal algorithms. One promising direction is to use methods that
construct new features, learning from successes of different algorithms,
extracting knowledge from indirect, partial learning and using it to build
final potential solutions. Another interesting aspect in the construction of
complex computational intelligence methods is dealing with different levels
of abstraction; useful meta-knowledge may come in the form of highly
abstract heuristic knowledge directing the search process for the optimal
model, or may be hidden in the details of algorithm implementation.

The main subjects of interest are:

.         Meta-learning algorithms and system architectures.

.         Meta-knowledge representation, acquisition, application, re-use
and construction, analysis of the usefulness of knowledge.

.         Knowledge transfer, knowledge sharing, transfer learning.

.         Meta-learning for big data.

.         Multimodel architectures, integration of hierarchy of individual
models for data mining.

.         Multimodel data mining systems/algorithms, which integrate several
methods of data analysis at different levels of granularity.

.         Data mining that use hybrid/heterogenous models

.         Advanced architectures of data mining systems. Combinations of
machine learning, neural networks, fuzzy systems, etc.

.         Transformation-base learning, including deep learning algorithms.

.         Extraction and construction of new features that simplify the
complex learning process, including pre-processing methods, multimodal
signal processing, extraction of information from specific types of data.

.         Methods of reasoning for automatic creation of decision models,
estimation of usefulness of knowledge for a given problem.

.         Applications to challenging problems, methods for testing complex
systems.

The session is not strictly limited to the above subjects. Every aspects of
meta-learning or other integration of learning algorithms and knowledge are
welcome.

IMPORTANT DATES


December 20, 2013:

Paper submission deadline.


March 15, 2014:

Notification of paper acceptance.


April 15, 2014:

Final manuscript submission deadline.

 

Organizers:

Norbert Jankowski and Wlodzislaw Duch
Department of Informatics, Nicolaus Copernicus University
ul. Grudziadzka 5, 87-100 Toruń, Poland
{norbert,wduch} @ is.umk.pl

 

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