Multi-Objective Supervised Learning
Fieldsend, Jonathan E.
Everson, Richard M.
Natural Computing series
Springer Berlin Heidelberg
Natural Computing Series
This chapter sets out a number of the popular areas in multiobjective supervised learning. It gives empirical examples of model complexity optimization and competing error terms, and presents the recent advances in multi-class receiver operating characteristic analysis enabled by multiobjective optimization. It concludes by highlighting some specific areas of interest/concern when dealing with multiobjective supervised learning problems, and sets out future areas of potential research.
Copyright © 2008 Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.com
Book title: Multiobjective Problem Solving from Nature
Extended version of the 2006 workshop paper presented at the Workshop on Multiobjective Problem-Solving from Nature, 9th International Conference on Parallel Problem Solving from Nature (PPSN IX), Reykjavik, Iceland, 9-13 September 2006; see: http://hdl.handle.net/10871/11785
pp. 155 - 176