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I have KDD dataset for detecting fraud actions on networks but it has millions of lines and >20 feature columns. Thus it is not viable to process all these on my personal computer. I am thinking about the random sampling on data to reduce the rows to some legible numbers than I want also to reduce the number of features. I know PCA can be used to reduce the feature number but first I also want to get rid of the some dirty features that are not helpful enough to classification purpose.

What are the algorithms can be used for that feature elimination and extraction purpose?

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I feel this is Not Constructive because answers could be numerous and what is "good" is subjective and to some extent determined by the individual applications or data sets to which a method will be applied. Try to focus the question to something more specific, less open ended. – Gavin Simpson Oct 19 '12 at 14:58
@Erogol: good question i will try to answer. Do you have a link to that dataset? – user603 Oct 19 '12 at 16:16
@use603: kdd.ics.uci.edu/databases/kddcup99/kddcup99.html is the link – Erogol Oct 20 '12 at 11:38

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