Multi-Objective Machine Learning
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.
Multi-objective machine learning covers the following main aspects:
- 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 ensembles
- Multi-objective learning to deal with tradeoffs between plasticity and
stability, long-term and short-term memories, specialization and
 Y. Jin, B. Sendhoff.
Pareto-based multi-objective machine learning: An overview and case studies.
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications
and Reviews, 38(3):397-415, 2008. Also here
 Y. Jin (Editor).
Multi-Objective Machine Learning. Springer, Berlin Heidelberg, 2006
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