HYBRID WATER CYCLE OPTIMIZATION ALGORITHM WITH SIMULATED ANNEALING FOR SPAM EMAIL DETECTION
Spam is referred to unsolicited commercial e-mail from someone trying to give some information that the receiver did not expected. This kind of email usually defined as junk and unwanted. As a filtering step, the spam email filters are implemented in conjunction to reduce this type of e-mails. Un...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Online Access: | http://umt-ir.umt.edu.my:8080/jspui/bitstream/123456789/16013/1/Abstract.pdf http://umt-ir.umt.edu.my:8080/jspui/bitstream/123456789/16013/2/Full%20Thesis%20-%20GHADA%20HAMMAD%20AL-RAWASHDEH.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Spam is referred to unsolicited commercial e-mail from someone trying to give some
information that the receiver did not expected. This kind of email usually defined as
junk and unwanted. As a filtering step, the spam email filters are implemented in
conjunction to reduce this type of e-mails. Unfortunately, new spam email attributes
have caused the spam email filter characteristic insufficient and inefficient to handle
the large amount of email. This problem is due to the large number of features that the
spam classifier needs to evaluate. By the help of feature selection method, the number
of features can be reduced. However, the optimal number of features remains a problem
and requires further investigation. In this thesis, a new hybrid method has been
introduced to make the spam email feature selection more accurate by using the metaheuristic
feature selection optimization approach. The proposed method is based on the
hybridization of Water Cycle Algorithm with the Simulated Annealing to optimize the
results. This study used a methodology that included groundwork, induction,
improvement, assessment, and comparison quality. For the training and validation
datasets, cross-validation was performed, and seven datasets were used to evaluate the
suggested spam classification |
|---|