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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

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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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