-------- Original-Nachricht -------- Betreff: Call for Papers, Special session on "Multi-Objective Machine Learning" Datum: Mon, 7 Nov 2005 15:10:04 +0100 Von: Yaochu.Jin@honda-ri.de Firma: "OptimaNumerics" An: Computational Science Mailing List computational.science@lists.optimanumerics.com
Call for Papers
Special Session on "Multi-objective Machine Learning" 2006 International Joint Conference on Neural Networks (part of WCCI'06) July 16-21, Vancouver, Canada http://www.wcci2006.org/
Organized by Yaochu Jin (yaochu.jin@honda-ri.de) URL: http://www.soft-computing.de/CFP_SS_MOML.html
Motivation and 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 no later than January 31, 2005 through the conference webpage. Please notice me by sending me an email if you are interested in submitting a paper to the Special Session.
---------------------------------------------- Dr. Yaochu Jin, Principal Scientist Honda Research Institute Europe Carl-Legien-Str.30 63073 Offenbach/Main Germany Phone: +49-69-89011735 Fax: +49-69-89011749 Email: yaochu.jin@honda-ri.de ---------------------------------------------