Dimensionality Reduction by Feature Selection in Machine Learning

author: Dunja Mladenić, Artificial Intelligence Laboratory, Jožef Stefan Institute
published: Feb. 25, 2007,   recorded: February 2005,   views: 17211


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Dimensionality reduction is a commonly used step in machine learning, especially when dealing with a high dimensional space of features. The original feature space is mapped onto a new, reduced dimensioanllyity space and the examples to be used by machine learning algorithms are represented in that new space. The mapping is usually performed either by selecting a subset of the original features or/and by constructing some new features. This persentation deals with the first approach, feature subset selection. We provide a brief overview of the feature subset selection techniques that are commonly used in machine learning and give a more detailed description of feature subset selection used in machine learning on text data. Performance of some methods used is document categorization is illustrated by providing experimental comparison on real-world data collected from the Web.

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Comment1 ali, May 25, 2014 at 8:57 p.m.:


Comment2 Jake Sully, November 24, 2019 at 5:19 a.m.:

Got to love the hack that forced an assignment on this. It does not even display on modern browsers...

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