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Measures for Unsupervised Fuzzy-Rough Feature Selection
2009
2009 Ninth International Conference on Intelligent Systems Design and Applications
For supervised learning, feature selection algorithms attempt to maximise a given function of predictive accuracy. This function usually considers the ability of feature vectors to reflect decision class labels. It is therefore intuitive to retain only those features that are related to or lead to these decision classes. However, in unsupervised learning, decision class labels are not provided, which poses questions such as; which features should be retained? and, why not use all of the
doi:10.1109/isda.2009.45
dblp:conf/isda/MacParthalainJ09
fatcat:ovaugcbn35dqpdd2l26j3vw6ia