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The Naive Bayes Classifier Application to Text Classification Example: spam filtering

The Naive Bayes Classifier Application to Text Classification Example: spam filtering. Marius Bulacu. Kunstmatige Intelligentie / RuG. Bayes Formula. Conditional Likelihood of the data given the class. Prior probability of the class before seeing anything. Posterior

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The Naive Bayes Classifier Application to Text Classification Example: spam filtering

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  1. The Naive Bayes Classifier Application to Text Classification Example: spam filtering Marius Bulacu Kunstmatige Intelligentie / RuG

  2. Bayes Formula Conditional Likelihood of the data given the class Prior probability of the class before seeing anything Posterior probability of the class after seeing the data Unconditional probability of the data

  3. Medical example p(+disease) = 0.002 p(+test | +disease) = 0.97 p(+test | -disease) = 0.04 p(+test) = p(+test | +disease) * p(+disease) + p(+test | -disease) * p(-disease) = 0.97 * 0.002 + 0.04 * 0.97 = 0.00194 + 0.03992 = 0.04186 p(+disease | +test) = p(+test | +disease) * p(+disease) / p(+test) = 0.97 * 0.002 / 0.04186 = 0.00194 / 0.04186 = 0.046 p(-disease | +test) = p(+test | -disease) * p(-disease) / p(+test) = 0.04 * 0.998 / 0.04186 = 0.03992 / 0.04186 = 0.953

  4. “naive” assumption that X and Y are independent Accumulation of evidence

  5. Spam filtering Example of regular mail: From acd@essex.ac.uk Mon Nov 10 19:23:44 2003Return-Path: <alan@essex.ac.uk>Received: from serlinux15.essex.ac.uk (serlinux15.essex.ac.uk [155.245.48.17]) by tcw2.ppsw.rug.nl (8.12.8/8.12.8) with ESMTP id hAAIecHC008727; Mon, 10 Nov 2003 19:40:38 +0100 Apologies for multiple postings.> 2nd C a l l f o r P a p e r s>> DAS 2004>> Sixth IAPR International Workshop on> Document Analysis Systems>> September 8-10, 2004>> Florence, Italy>> http://www.dsi.unifi.it/DAS04>> Note:> There are two main additions with respect to the previous CFP:> 1) DAS&DL data are now available on the workshop web site> 2) Proceedings will be published by Springer Verlag in LNCS series

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  7. Learning to classify e-mail • Target concept Spam?: e-mail --> {-,+} • Each word represents an attribute characterizing the e-mail • Estimate p(+spam) and p(-spam) from the training data as well as the conditional likelihoods for all the encountered words • For a new e-mail, assuming naive Bayes conditional independence, compute the MAP hypothesis

  8. Conclusions • Effective: about 90% correct classification • Could be applied to any text classification problem • Needs to be polished

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