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:

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 Trial #1# of spam documents # of ham documents False positive rateAccuracy Training Set66743553.66%98.9% Testing Set82476 Trial #3# of spam documents # of ham documents False positive rateAccuracy Training Set2185707.02%96.9% Testing Set57232 Trial #2# of spam documents # of ham documents False positive rateAccuracy Training Set19711287.35%98.1% Testing Set68451

5. SVM RESULTS Trial #1# of spam documents # of ham documents False positive rateAccuracy Training Set66743552.43%99.6% Testing Set82476 Trial #3# of spam documents # of ham documents False positive rateAccuracy Training Set2185705.3%97.6% Testing Set57232 Trial #2# of spam documents # of ham documents False positive rateAccuracy Training Set19711285.9%99.3% Testing Set68451

6. WEAKNESS OF NAÏVE BAYES CLASSIFIER Example: hey man are you interested in sports? then email me at imcool@gmail.com imcool@gmail.com Spammers can avoid using words that are more prone to being in a spam email

7. WORK AHEAD  Finish implementing and testing Decision Tree  More preprocessing of the data  Perform more trials with different ratios of training set and testing set