Machine Learning Basics with Applications to Email Spam Detection

1. UGR Project - Haoyu li, brittanyedwards, weizhang under xiaoxiaoxu and aryenehorai Machine Learning Basics with Applications to Email Spam Detection

2. General background information about the process of machine learning

3. The process of email detection • Motivation of this project • Pre-processing of data • Classifier Models • Evaluation of classifiers

4. Motivation of this project • Spam email has been annoyed every personal email account • 60% of January 2004 emails were spam • Fraud & Phishing • Spam vs. Ham email

5. Our Goal

6. Spam Email example

7. Ham Email example

8. The process of email detection • Motivation of this project • Pre-processing of data • Classifier Models • Evaluation of classifiers

9. Pre-processing of data • Convert capital letters to lowercase • Remove numbers, and extra white space • Remove punctuations  • Remove stop-words • Delete terms with length greater than 20. 

10. Pre-processing of data • Original Email

11. Pre-processing of data • After pre-processing

12. Pre-processing of data • Extract Terms

13. Pre-processing of data • Reduce Terms • Keep word length <20

14. The process of email detection • Motivation of this project • Pre-processing of data • Classifier Models • Evaluation of classifiers

15. Different classification methods • K Nearest Neighbor (KNN) • Naive Bayes Classifier • Logistic Regression • Decision Tree Analysis

16. What is K Nearest Neighbor • Use k "closet" samples (nearest neighbors) to perform classification

17. What is K Nearest Neighbor

18. Initial outcome and strategies for improvement • KNN accuracy was ~64% - very low • KNN classifier does not fit our project  • Term-list is still too large  • Try different method to classify and see if evaluation results are better than KNN results • Continue to reduce size of term list by removing terms that are not meaningful

19. Steps for improvement • Remove sparsity • Reduced length threshold • Created hashtable • Used alternative classifier • Naive- Bayes Classifier

20. Hashtable • Calculate Hash Key for each term in term-list. • Once collision occurs, use the separate chain

21. Naive- Bayes classifier

22. Secondary Results • Correctness increases from 62% to 82.36%

23. Suggestions for further improvement • Revise pre-processing • Apply additional classifiers

24. Thank you • Questions?