Call for Papers
2006 International Joint Conference on Neural Networks (part of
WCCI06) , July 16 - 21, 2006,
Vancouver, Canada
Special Session on
Multi-Objective Machine Learning
Scope
Machine learning usually has to achieve multiple targets, which are often
conflicting with each other. For example in feature selection, minimizing
the number of features and the maximizing feature
quality are two conflicting objectives. It is also well realized that
model selection has to deal with the trade-off between model complexity
and approximation or classification accuracy.
Traditional learning algorithms attempt to deal with multiple objectives
by combining them into a scalar cost function so that multi-objective
machine learning problems are reduced to single-objective problems.
Recently, increasing interest has been shown in applying Pareto-based
multi-objective optimization to machine learning, particularly inspired
by the successful developments in evolutionary multi-objective optimization.
It has been shown that the multi-objective approach to machine learning
is particularly successful in 1) improving the performance of the traditional
single-objective machine learning methods 2) generating highly diverse
multiple Pareto-optimal models for constructing ensembles and,
3) in achieving a desired trade-off between accuracy and interpretability
of neural networks or fuzzy systems.
This proposed special session intends to further promote research interests
in multi-objective machine learning by presenting the most recent research
results and discussing the main challenges in this area. Topics include
but are not limited to
- Multi-objective clustering, feature extraction and feature selection
- Multi-objective model selection to improve the performance of learning
models, such as neural networks, support vector machines, decision trees,
and fuzzy systems
- Multi-objective model selection to improve the interpretability of
learning models, e.g., to extract symbolic rules from neural networks,
or to improve the interpretability of fuzzy systems
- Multi-objective generation of learning ensembles
- Multi-objective learning to deal with tradeoffs between plasticity and
stability, long-term and short-term memories, specialization and
generalization
- Multi-objective machine learning applications
Submission
All special session papers must be submitted
through the
WCCI submission webpage, where Instructions for Authors are also
available. Note that the space limit is now 8 pages .
Please choose "S.Special Sessions, Sa: Multi-objective machine learning" as
your main research topic. Please notice me beforehand by sending me an email
if you are interested in submitting a paper to the Special Session.
Important dates:
- Submission: January 31, 2006 The submission deadline
has been extended to February 15, 2006
- Notification: March 15, 2006
- Camera ready: April 15, 2006
Special Session Organizer
Dr. Yaochu Jin
Honda Research Institute Europe
Carl-Legien-Str. 30
63073 Offenbach/Main
GERMANY
Email: yaochu.jin<< at> >honda-ri.de
Special Session Review Committee
- Fernando J. Barrionuevo, Spain
- Ester Bernado-Mansilla, Spain
- Arjun Chandra, UK
- Jonathan Fieldsend, UK
- Christian Igel, Germany
- Yaochu Jin, Germany
- Urszula Markowska Kaczmar, Poland
- DaeEun Kim, Germany
- Sam Kwong, Hong Kong
- Luiz Oliveira, Brasil
- Yang Zhang, UK