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From "Andre-Philippe Paquet (Created) (JIRA)" <j...@apache.org>
Subject [jira] [Created] (MAHOUT-826) Bayes/CBayes classification on a non-existing feature
Date Mon, 03 Oct 2011 19:10:34 GMT
Bayes/CBayes classification on a non-existing feature
-----------------------------------------------------

                 Key: MAHOUT-826
                 URL: https://issues.apache.org/jira/browse/MAHOUT-826
             Project: Mahout
          Issue Type: Bug
          Components: Classification
    Affects Versions: 0.5
            Reporter: Andre-Philippe Paquet
            Priority: Minor


(see http://comments.gmane.org/gmane.comp.apache.mahout.user/9597)

Using CBayes or Bayes, when trying to classify a feature/word that doesn't exist in the model,
instead of returning the default/unknown label, the algorithm returns all labels with a constant
score (ex: 12.386649147018964). After a quick look in CBayesAlgorithm, I found the problem
in the featureWeight function that returns the theta normalized weight even if the feature
didn't have any match (result=0).

As a fix, I overrided the function in a subclass and return 0 if the weight of the current
feature in the current label is 0. 

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