Feature selection method using support vector machine classifier
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United States of America Patent
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Jun 2, 2009
Grant Date -
Feb 7, 2008
app pub date -
Aug 21, 2007
filing date -
May 1, 1998
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Abstract
Identification of a determinative subset of features from within a large set of features is performed by training a support vector machine to rank the features according to classifier weights, where features are removed to determine how their removal affects the value of the classifier weights. The features having the smallest weight values are removed and a new support vector machine is trained with the remaining weights. The process is repeated until a relatively small subset of features remain that is capable of accurately separating the data into different patterns or classes. The method is applied for selecting the smallest number of genes that are capable of accurately distinguishing between medical conditions such as cancer and non-cancer.
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