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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2074/1194
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| Title: | Feature subset selection using a new definition of classifiability |
| Authors: | Dong, Ming
Kothari, Ravi |
| Keywords: | Feature selection
Dimensionality reduction
Classification |
| 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... |
| URI: | http://eprint.iitd.ac.in/dspace/handle/2074/1194 |
| Appears in Collections: | Computer Science and Engineering
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