Semantic feature selection for spam filtering
(Spam or unsolicited e-mail could be in a form of advertisement, product promotions, etc. It has become a key problem for e-mail users. Due to this, spam filtering has become a major research attention. In this research, spam filtering is explored based on semantic feature selection. Here, the W...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2010
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/14865/1/Azlina%20Narawi%20ft.pdf |
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| Summary: | (Spam
or unsolicited e-mail could be in a form of advertisement, product promotions,
etc. It has become a key problem for e-mail users. Due to this, spam filtering has
become a major research attention. In this research, spam filtering is explored based
on semantic feature selection. Here, the Wordnet-based approach is employed with
statistical approaches used for the purpose of comparison. In further enhancing the
task, another technique using distributed clustering has been proposed for
identifying meaningful words for characterization) A series of experiments were
conducted. The results show that the WordNet-based approach is able to select more
meaningful features as compared to statistical approaches. The WordNet-based
approach has the ability to achieve great dimensionality. A reduction of 72.9 % and
49.2% for the non-spam and spam categories was achieved respectively. Pruning of
features by incorporating distributed clustering enhanced performance significantly.
A new framework for semantics filtering was proposed as a result with distinct
features in Spam and non-spam e-mail documents were determined. The promising
results achieved, show that this approach can be further explored on other datasets
or applications. |
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