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A comparison of ANN, Naïve Bayes, and Decision Tree for the purpose of spam filtering

A comparison of ANN, Naïve Bayes, and Decision Tree for the purpose of spam filtering. Kaashyapee jha ECE/CS 539. Naïve Bayes Classifier. Bayes Theorem:. Method : P(S|D )= ( 2 ) P(S|D) = P( S|D) = (3) . PreProcessing.

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A comparison of ANN, Naïve Bayes, and Decision Tree for the purpose of spam filtering

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  1. A comparison of ANN, Naïve Bayes, and Decision Tree for the purpose of spam filtering Kaashyapee jha ECE/CS 539 1

  2. Naïve Bayes Classifier Bayes Theorem: Method: P(S|D)= (2) P(S|D) = P(S|D) = (3)

  3. PreProcessing • Stop list: do not take into account trivial words like {or, and, but, a, an, the, is, in, for} • Do not take into account words that are very uncommon

  4. Naïve Bayes Classifier Results

  5. SVM Results

  6. Weakness of Naïve Bayes Classifier • Example: hey man are you interested in sports? then email me at imcool@gmail.com • Spammers can avoid using words that are more prone to being in a spam email

  7. Work Ahead • Finishimplementing andtesting Decision Tree • More preprocessing of the data • Perform more trials with different ratios of training set and testing set

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