Fuzzy Granular Classifier Approach for Spam Detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F15%3A50004184" target="_blank" >RIV/62690094:18450/15:50004184 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007/978-3-319-24306-1_25" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-319-24306-1_25</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-24306-1_25" target="_blank" >10.1007/978-3-319-24306-1_25</a>
Alternative languages
Result language
angličtina
Original language name
Fuzzy Granular Classifier Approach for Spam Detection
Original language description
Spam email problem is a major shortcoming of email technology for computer security. In this research, a granular classifier model is proposed to discover hyperboxes in the geometry of information granules for spam detection in three steps. In the firststep, the k-means clustering algorithm is applied to find the seed_points to build the granular structure of the spam and non-spam patterns. Moreover, applying the interval analysis through the high homogeneity of the patterns captures the key part of the spam and non-spam classifiers' structure. In the second step, PSO algorithm is hybridized with the k-means to optimize the formalized information granules' performance. The proposed model is evaluated based on the accuracy, misclassification and coverage criteria. Experimental results reveal that the performance of our proposed model is increased through applying Particle Swarm Optimization and fuzzy set.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Computational collective intelligence. Part II.
ISBN
978-3-319-24306-1
ISSN
0302-9743
e-ISSN
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Number of pages
9
Pages from-to
256-264
Publisher name
Springer
Place of publication
Berlin
Event location
Madrid
Event date
Sep 21, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000366123600025