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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 di#erently 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... (Update)
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BibTeX entry: (Update)
@misc{ mierswa-information,
author = "Ingo Mierswa and Michael Wurst",
title = "Information Preserving Multi-Objective Feature Selection for Unsupervised
Learning",
url = "citeseer.ist.psu.edu/mierswa06information.html" }
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