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|Title: ||Feature subset selection using a new definition of classifiability|
|Authors: ||Dong, Ming|
|Keywords: ||Feature selection|
|Issue Date: ||2003 |
|Citation: ||Pattern Recognition Letters, 24(9-10), 1215-1225|
|Abstract: ||The performance of most practical classifiers improves when correlated or irrelevant features are removed. Machine based classification is thus often preceded by subset selection––a procedure which identifies relevant features of a high dimensional data set. At present, the most widely used subset selection technique is the so-called "wrapper" approach in which a search algorithm is used to identify candidate subsets and the actual classifier is used as a "black box" to evaluate the fitness of the subset. Fitness evaluation of the subset however requires cross-validation or other resampling based procedure for error estimation necessitating the construction of a large number of classifiers for each subset. This significant computational burden makes the wrapper approach impractical when a large number of features are present.
In this paper, we present an approach to subset selection based on a novel definition of the classifiability of a given data. The classifiability measure we pr...|
|Appears in Collections:||Computer Science and Engineering|
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