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The document discusses techniques for classifying and filtering email messages. It describes using a Naive Bayes classifier with word frequency statistics to efficiently classify messages into folders. The key is leveraging each user's personal filtering preferences through supervised training on their labeled messages. The system was tested on over 7,000 messages across four users, achieving 89% average accuracy at filtering speed of 259 messages per second.

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E-Mail Filtering Soonyeon Kim
Good Site for Data Mining ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Text Mining: Finding Nuggets in Mountains of Textual Data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Brief Description ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Ifile: An Application of Machine Learning to E-mail Filtering ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Outline of Paper ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Classification Efficiency ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Classification Efficiency(2) ,[object Object],[object Object],[object Object],[object Object]
Personal E-mail Filtering ,[object Object],[object Object],[object Object],[object Object],[object Object]
Learning Architecture ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Features ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],f
Features(2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Maintaining Dictionary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Maintaining Dictionary(2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],two msg in A - "party party belch yellow yellow" one msg in B - "party kick" one msg in C - "peep peep yellow yellow yellow"
Word Selection ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Tokenizing text ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Naïve Bayes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Naïve Bayes-1 st step ,[object Object],[object Object]
Naïve Bayes-second step ,[object Object],[object Object],[object Object],[object Object],[object Object]
Naïve Bayes ,[object Object],[object Object],[object Object],[object Object],[object Object]
Experimental Result ,[object Object],[object Object]
Experimental Result
Experimental Result ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Experimental Result ,[object Object],[object Object],[object Object],[object Object]
Experimental Result ,[object Object],[object Object],[object Object],[object Object],[object Object]
Experimental Result ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Filtering Junk E-Mail Soonyeon Kim
A Bayesian Approach to Filtering Junk E-Mail ,[object Object],[object Object]
Problems of Junk-mail ,[object Object],[object Object],[object Object]
Machine Learning Approach ,[object Object],[object Object],[object Object]
Bayesian Classifier ,[object Object],[object Object],[object Object],[object Object]
Bayesian networks ,[object Object],[object Object]
Bayesian Rule ,[object Object],[object Object]
Features ,[object Object],[object Object]
Features(2) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Feature Selection ,[object Object],[object Object]
Evaluation ,[object Object],[object Object],[object Object],[object Object]
Using different features ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Using different features ,[object Object]
Using different features ,[object Object],[object Object],[object Object],D (true negatives) C (false positives) actually Legit B (false negatives) A (true positives) actually Junk predict Legit predict Junk
Using different features ,[object Object]
Sub-classes of junk E-mail ,[object Object],[object Object],[object Object]
Sub-classes of junk E-mail ,[object Object]
Real Usage Scenario ,[object Object],[object Object],[object Object]
Real Usage Scenario ,[object Object]

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