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Efficient handling of high-dimensional feature spaces by randomized classifier ensembles
2002
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '02
Handling massive datasets is a difficult problem not only due to prohibitively large numbers of entries but in some cases also due to the very high dimensionality of the data. Often, severe feature selection is performed to limit the number of attributes to a manageable size, which unfortunately can lead to a loss of useful information. Feature space reduction may well be necessary for many stand-alone classifiers, but recent advances in the area of ensemble classifier techniques indicate that
doi:10.1145/775047.775093
dblp:conf/kdd/KolczSK02
fatcat:m64gbpf5yje7pmexyaslgu6da4