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Manual Reference Pages  -  AI::CATEGORIZER::LEARNER::NAIVEBAYES (3)

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NAME

AI::Categorizer::Learner::NaiveBayes - Naive Bayes Algorithm For AI::Categorizer

CONTENTS

SYNOPSIS



  use AI::Categorizer::Learner::NaiveBayes;
 
  # Here $k is an AI::Categorizer::KnowledgeSet object
 
  my $nb = new AI::Categorizer::Learner::NaiveBayes(...parameters...);
  $nb->train(knowledge_set => $k);
  $nb->save_state(filename);
 
  ... time passes ...
 
  $nb = AI::Categorizer::Learner::NaiveBayes->restore_state(filename);
  my $c = new AI::Categorizer::Collection::Files( path => ... );
  while (my $document = $c->next) {
    my $hypothesis = $nb->categorize($document);
    print "Best assigned category: ", $hypothesis->best_category, "\n";
    print "All assigned categories: ", join(, , $hypothesis->categories), "\n";
  }



DESCRIPTION

This is an implementation of the Naive Bayes decision-making algorithm, applied to the task of document categorization (as defined by the AI::Categorizer module). See AI::Categorizer for a complete description of the interface.

This module is now a wrapper around the stand-alone Algorithm::NaiveBayes module. I moved the discussion of Bayes’ Theorem into that module’s documentation.

METHODS

This class inherits from the AI::Categorizer::Learner class, so all of its methods are available unless explicitly mentioned here.

new()

Creates a new Naive Bayes Learner and returns it. In addition to the parameters accepted by the AI::Categorizer::Learner class, the Naive Bayes subclass accepts the following parameters:
o threshold

Sets the score threshold for category membership. The default is currently 0.3. Set the threshold lower to assign more categories per document, set it higher to assign fewer. This can be an effective way to trade of between precision and recall.

threshold()

Returns the current threshold value. With an optional numeric argument, you may set the threshold.

train(knowledge_set => CW$k)

Trains the categorizer. This prepares it for later use in categorizing documents. The knowledge_set parameter must provide an object of the class AI::Categorizer::KnowledgeSet (or a subclass thereof), populated with lots of documents and categories. See AI::Categorizer::KnowledgeSet for the details of how to create such an object.

    categorize($document)

Returns an AI::Categorizer::Hypothesis object representing the categorizer’s best guess about which categories the given document should be assigned to. See AI::Categorizer::Hypothesis for more details on how to use this object.

    save_state($path)

Saves the categorizer for later use. This method is inherited from AI::Categorizer::Storable.

CALCULATIONS

The various probabilities used in the above calculations are found directly from the training documents. For instance, if there are 5000 total tokens (words) in the sports training documents and 200 of them are the word curling, then P(curling|sports) = 200/5000 = 0.04 . If there are 10,000 total tokens in the training corpus and 5,000 of them are in documents belonging to the category sports, then P(sports) = 5,000/10,000 = 0.5> .

Because the probabilities involved are often very small and we multiply many of them together, the result is often a tiny tiny number. This could pose problems of floating-point underflow, so instead of working with the actual probabilities we work with the logarithms of the probabilities. This also speeds up various calculations in the categorize() method.

TO DO

More work on the confidence scores - right now the winning category tends to dominate the scores overwhelmingly, when the scores should probably be more evenly distributed.

AUTHOR

Ken Williams, ken@forum.swarthmore.edu

COPYRIGHT

Copyright 2000-2003 Ken Williams. All rights reserved.

This library is free software; you can redistribute it and/or modify it under the same terms as Perl itself.

SEE ALSO

AI::Categorizer(3), Algorithm::NaiveBayes(3)

A re-examination of text categorization methods by Yiming Yang <http://www.cs.cmu.edu/~yiming/publications.html>

On the Optimality of the Simple Bayesian Classifier under Zero-One Loss by Pedro Domingos /www.cs.washington.edu/homes/pedrod/mlj97.ps.gz in http:

A simple but complete example of Bayes’ Theorem from Dr. Math /www.mathforum.com/dr.math/problems/battisfore.03.22.99.html in http:

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