1. Group 2 R95922027 李庭閣 R95922034 孔垂玖 R95922081 許守傑 R95942129 鄭力維

2.  Experiment setting  Feature extraction  Model training  Hybrid-Model  Conclusion  Reference

3.  Selected online corpus: enron  Removing html tags  Factoring important headers  Six folders from enron1 to enron6.  Contain totally 13496 spam mails & 15045 ham mails

4.  Experiment setting  Feature extraction  Model training  Hybrid-Model  Conclusion  Reference

5. 1. Transmitted Time of the Mail 2. Number of the Receiver 3. Existence of Attachment 4. Existence of images in mail 5. Existence of Cited URLs in mail 6. Symbols in Mail Title 7. Mail-body

6. Spam: Non-uniform D istribution Spam: Only Single Receiver

8. AttachmentImageURL Spam 0.0307%0.6816%30.779% Ham 7.3712%0%7.0521%

9. MarksProbability of being Spam Mail Feature Showing Rate ~ ^ | * % [] ! ? =0.91128% in spam \ / ; &0.18216% in ham  Title Absentness  Spam senders add titles now.  Arabic Numeral :  Almost equal probability (Date, ID)  Non-alphanumeric Character & Punctuation Marks: Appear more often in Spam Appear more often in ham

10.  Build the internal structure of words  Use a good NLP tool called Treetagger to help us do word stemming  Given the stemmed words appeared in each mail, we build a sparse format vector to represent the “semantic” of a mail

11.  Experiment setting  Feature extraction  Model training  Hybrid-Model  Conclusion  Reference

12. Given a bag of words (x 1, x 2, x 3,…,x n ), Naïve Bayes is powerful for document classification.

13. Create a word-document (mail) matrix by SRILM. For every mail (column) pair, a similarity value can be calculated.

14. As K = 1, the KNN classification model show the best accuracy.

15. Maximize the entropy and minimize the Kullback-Leiber distance between model and the real distribution. The elements in word-document matrix are modified to the binary value {0, 1}.

16. Binary : Select binary value {0,1} to represent that this word appears or not Normalized : Count the occurrence of each word and divide them by their maximum occurrence counts.

17.  Experiment setting  Feature extraction  Model training  Hybrid-Model  Conclusion  Reference

18. The accuracy of NN-based Hybrid Model is always the highest.

19. The voting model averages the classification result, promoting the ability of the filter slightly. However, sometimes voting might reduce the accuracy because of misjudgments of majority. 1.Knn + naïve Bayes + Maximum Entropy 2.naïve Bayes + Maximum Entropy + SVM

20.  Experiment setting  Feature extraction  Model training  Hybrid-Model  Conclusion  Reference

21.  7 features are shown mail type discrimination.  Transmitted Time & Receiver Size  Attachment, Image, and URL  Non-alphanumeric Character & Punctuation Marks  5 populous Machine Learning are proved suitable for spam filter  Naïve Bayes, KNN, SVM  2 Model combination ways are tested.  Committee-based & Single Neural Network

22.  [1]. M. Sahami, S. Dumais, D. Heckerman, and E. Horvitz, "A Bayesian Approach to Filtering Junk E- Mail," in Proc. AAAI 1998, Jul. 1998.  [2] A plan for spam: http://www.paulgraham.com/spam.html http://www.paulgraham.com/spam.html  [3]Enron Corpus: http://www.aueb.gr/users/ion/ http://www.aueb.gr/users/ion/  [4]Treetagger: http://www.ims.uni- stuttgart.de/projekte/corplex/TreeTagger/DecisionTreeTagger.html  [5]Maximum Entropy: http://homepages.inf.ed.ac.uk/s0450736/maxent_toolkit.html http://homepages.inf.ed.ac.uk/s0450736/maxent_toolkit.html  [6]SRILM: http://www.speech.sri.com/projects/srilm/ http://www.speech.sri.com/projects/srilm/  [7]SVM: http://svmlight.joachims.org/http://svmlight.joachims.org/