1. Spam Detection
Ethan Grefe
December 13, 2013

2. Motivation Spam email is constantly cluttering inboxes
Commonly removed using rule based filters
Spam often has very similar characteristics
This allows them to be detected using machine learning
Naïve Bayes Classifiers
Support Vector Machines

3. SVM Solution Used training data from CSDMC2010 SPAM corpus
4327 labeled s
2949 non-spam messages (HAM)
1378 spam messages (SPAM).
Extracted features from the subject and body of s
Used resulting feature vectors to train an SVM classifier in Matlab

4. Email Features Features were determined by research and observation
Best results were obtained with the following features
Percentage of letters that are capitalized
Types of punctuation used
Average length of a word
Amount of html in the

5. Classifier Results Trained on a random 35% of emails
Tested SVM classifier on remaining 65%
Trained SVM using three different kernel functions
Kernel Function
Spam Classification Rate
Ham Classification Rate
Total Classification Rate
RBF
80.06%
92.33%
86.20%
Linear
78.69%
80.66%
79.67%
Quadratic
82.75%
84.85%
83.80%

6. Possible Improvements
Use Naïve Bayes to classify s using word frequency
Obtain a wider variety of input features
Test other types of learning algorithms