Fuzzy Granular Classifier Approach for Spam Detection

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Fuzzy Granular Classifier Approach for Spam Detection

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Fuzzy Granular Classifier Approach for Spam Detection

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Computational collective intelligence. Part II.

ISBN

978-3-319-24306-1

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

9

Strana od-do

256-264

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Madrid

Datum konání akce

21. 9. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000366123600025