From: Jason Frankovitz <jason@...>
Date: 2007-02-07T02:43:52+09:00
Subject: Re: Classifier::Bayes - handling "none of the above" cases

Giles Bowkett wrote:
>> above" class, then you'd need to provide examples from that none of the
>> above class. It's not so easy to decide what's representative of "none of
>> the above", and even if you could do so, it would probably violate the
>> assumptions of the classifier and lead to reduced performance. Thus, we
>> have to come up with more creative problem-specific solutions to handle
>> something resembling a "none-of-the-above" case, usually solutions that
>> change the definition of the problem quite dramatically.
> 
> Really a "none of the above" filter is of limited usefulness.
> Categories in Bayesian classifiers are all about compartmentalization.
> The goal isn't really categorization, it's training the filter. You
> really want the filter to separate on an unambiguous difference, like
> "spam" vs. "not spam," because this will teach the filter to
> differentiate unambiguously. That's what Bayesian filters are good at
> doing.
> 
> Giving a Bayesian classifier a "none of the above" category will just
> confuse it. It doesn't work by checking category A, then category B,
> then finally category C. It works by aggregating data and extracting
> probabilistic similarity. The features shared by the "none of the
> above" will be too varied and numerous for any similarity to be
> extracted. Instead of all the stuff that doesn't fit anywhere else
> going into "none of the above," **everything** will run a risk of
> going into "none of the above," because "none of the above" will be
> too vaguely specified to be dissimilar from anything else.
> 
> Really you would either want to use the classifier differently, or use
> a different technique altogether.

First of all Giles and Ken, thanks for your answers. It sounds like a 
Bayesian approach won't work for what I want to do. This same gem has 
another classifer inside it, called Classifier::LSI which does latent 
semantic indexing. I don't know much about it yet other than it's not as 
fast or as small as a Bayesian classifier. However, would it be more 
suited to supporting a "none of the above" feature?

Or would you recommend something entirely different?

Many thanks,
-Jason

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