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Building out a system that tries to apply zero or more predefined labels to text.

For each label, we've:

  • built out a reasonably good vocabulary of high-value words/features
  • developed a corpus containing thousands of labeled entries
  • trained a NaiveBayesClassifier for each topic that does a good job of classifying valid vs noisy content

The problem seems to be that the individual classifier is great at differentiating between valid & noisy content WITHIN a topic:

  • "the green energy bill will revolutionize..." (green = "green energy")
  • "the green bay packers went on to lose their..." (green != "green energy")

...but when classifying content that shouldn't match ANY topic it has a very high rate of false positives. There's no "everything else" label!

tl;dr it's good at subtle, in-topic differentiation, but terrible at broad topic labeling

Are there any algorithms that help you classify into N categories, but allow for "everything else" which might not fit into ANY of the categories?

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Looks like the common approaches to multi-class classification actually solve this challenge.

Building individual Naive Bayes Classifiers with only the training data for a single label is insufficient - we must also the include data from other labels as "everything else".

See Text-Classification-Problem, what is the right approach?

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