Ugr project haoyu li brittany edwards wei zhang under xiaoxiao xu and arye nehorai
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UGR Project - Haoyu li, brittany edwards , wei zhang under xiaoxiao xu and arye nehorai. Machine Learning Basics with Applications to Email Spam Detection. General background information about the process of machine learning. The process of email detection. Motivation of this project

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Ugr project haoyu li brittany edwards wei zhang under xiaoxiao xu and arye nehorai

UGR Project - Haoyu li, brittanyedwards, weizhang under xiaoxiaoxu and aryenehorai

Machine Learning Basics with Applications to Email Spam Detection



The process of email detection
The process of email detection learning

  • Motivation of this project

  • Pre-processing of data

  • Classifier Models

    • Evaluation of classifiers


Motivation of this project
Motivation of this project learning

  • Spam email has been annoyed every personal email account

    • 60% of January 2004 emails were spam

    • Fraud & Phishing

  • Spam vs. Ham email


Our goal
Our Goal learning




The process of email detection1
The process of email detection learning

  • Motivation of this project

  • Pre-processing of data

  • Classifier Models

    • Evaluation of classifiers


Pr e processing of data
Pr learninge-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. 


Pr e processing of data1
Pr learninge-processing of data

  • Original Email


Pr e processing of data2
Pr learninge-processing of data

  • After pre-processing


Pr e processing of data3
Pr learninge-processing of data

  • Extract Terms


Pr e processing of data4
Pr learninge-processing of data

  • Reduce Terms

    • Keep word length <20


The process of email detection2
The process of email detection learning

  • Motivation of this project

  • Pre-processing of data

  • Classifier Models

    • Evaluation of classifiers


Different classification methods
Different classification methods learning

  • K Nearest Neighbor (KNN)

  • Naive Bayes Classifier

  • Logistic Regression

  • Decision Tree Analysis


What is k nearest neighbor
What is K Nearest Neighbor learning

  • Use k "closet" samples (nearest neighbors) to perform classification



Initial outcome and strategies for improvement
Initial outcome and strategies for improvement learning

  • 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


Steps for improvement
Steps for learningimprovement

  • Remove sparsity

  • Reduced length threshold

  • Created hashtable

  • Used alternative classifier

    • Naive- Bayes Classifier


Machine learning basics with applications to email spam detection

Hashtable learning

  • Calculate Hash Key for each term in term-list.

  • Once collision occurs, use the separate chain


Naive bayes classifier
Naive- Bayes learningclassifier


Secondary r esults
Secondary learningResults

  • Correctness increases from 62% to 82.36%


Suggestions for further improvement
Suggestions for further improvement learning

  • Revise pre-processing

  • Apply additional classifiers


Thank you
Thank you learning

  • Questions?