Information preserving multi-objective feature selection for unsupervised learning

Authors:
Ingo Mierswa;Michael Wurst
Affiliations:
University of Dortmund;University of Dortmund
Venue:
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Year:
2006

Citing 11
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Abstract

In this work we propose a novel, sound framework for evolutionary feature selection in unsupervised machine learning problems. We show that unsupervised feature selection is inherently multi-objective and behaves differently from supervised feature selection in that the number of features must be maximized instead of being minimized. Although this might sound surprising from a supervised learning point of view, we exemplify this relationship on the problem of data clustering and show that existing approaches do not pose the optimization problem in an appropriate way. Another important consequence of this paradigm change is a method which segments the Pareto sets produced by our approach. Inspecting only prototypical points from these segments drastically reduces the amount of work for selecting a final solution. We compare our methods against existing approaches on eight data sets.