Revision history for Perl extension AI::Categorize::NaiveBayes.

0.05


  - Made lots of improvements to the NaiveBayes categorizer.  It was
so bad as to be essentially useless before.  Now it is scoring better
in F1, accuracy, and running time than the kNN categorizer on my
standard test corpus.  This improvement came from studying Tom
Mitchell's excellent book "Machine Learning".

        01-NaiveBayes: F1=0.195  accuracy=0.981  time=  99 sec
               02-kNN: F1=0.169  accuracy=0.889  time=1199 sec

  - Increased the efficiency of the category map.  Added boolean
is_in_category() and contains_document() methods.

  - Fixed a bug in the AI::Categorize::Evaluate class in which
default arguments weren't being passed properly to the created
classes.

  - Cleaned up the formatting of the AI::Categorize::Evaluate output,
and added the accuracy score.

  - Fixed a small problem in kNN in which it was using k-1 similar
documents instead of k.

  - Added an accuracy() and error() method to AI::Categorize.
Calculates the accuracy/error over all binary category membership
decisions.  Has the same interface as the previous F1() method.

  - Fixed the F1() method to return 1 (perfect score) when you
correctly assign zero categories.

  - Added a cat_map() method to AI::Categorize class, which returns
the AI::Categorize::Map object so you can query this information.

0.04
  - Reworked the AI::Categorize::Evaluate module so that it much better 
    addresses the issue of how to specify both general info for all tests 
    and specific info for each test.  This makes it possible to test the results
    of using different initialization parameters, for instance, or the results on
    varying test sets.

  - Made some changes to the way AI::Categorize::Evaluate stores its results
    between stages of the testing.  This isn't stable yet.

  - Added a testing summary at the end of AI::Categorize::Evaluate->evaluate_test_set.

  - Created the 'drmath-1.00' corpus, which I'll use as a stable corpus for 
    benchmarking the differences various changes to the code has.  It's large, 
    so I'm not distributing it with the modules.  Write me if you want it.

  - The kNN and NaiveBayes classifiers now trim their list of corpus features 
    (words) to get rid of seldom-used features.  This can improve speed
    and quality.  Preliminary results (using F1 as a quality measure) are:
       corpus is drmath-1.00 with 12379 unique features.
        kNN using 100% of features: F1=0.180, testing time=1384 sec
        kNN using  20% of features: F1=0.178, testing time=1060 sec
        kNN using  10% of features: F1=0.180  testing time=1050 sec
        NB  using 100% of features: F1=0.037, testing time= 102 sec
        NB  using  20% of features: F1=0.041, testing time=  72 sec
        NB  using  10% of features: F1=0.039, testing time=  93 sec
    See the 'features_kept' item in the kNN and NaiveBayes docs.

  - Created the new AI::Categorize::VectorBased class, which kNN now inherits
    from, and which can be a base class for other classifiers (like SVM, hint 
    hint).

  - Started to clean up print() statements throughout the code.  They give feedback
    on training progress, but sometimes you probably don't want to see it.

  - Moved the example script 'evaluate.pl' to the new 'eg/' directory, because
    otherwise 'make install' would install it into site_perl/ .  If you installed
    previous versions of AI::Categorize, you may want to remove 'evaluate.pl'
    from your site_perl/ directory.


0.03  Tue May 22 18:01:46 CDT 2001
   - First release to CPAN
   - Added 'make test' procedure
   - Wrote docs for the major classes

0.02
   - Added AI::Categorize::kNN class
   - Added AI::Categorize::Evaluate class and the evaluate.pl script

0.01  Thu Apr 12 23:42:11 2001
	- original version; created by h2xs 1.1.1.4 with options
		-XA -n AI::Categorize::NaiveBayes
        
        - Not released to CPAN.