Spam filtering in social networks using regularized deep neural networks with ensemble learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F18%3A39913513" target="_blank" >RIV/00216275:25410/18:39913513 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-319-92007-8_4" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-92007-8_4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-92007-8_4" target="_blank" >10.1007/978-3-319-92007-8_4</a>
Alternative languages
Result language
angličtina
Original language name
Spam filtering in social networks using regularized deep neural networks with ensemble learning
Original language description
Spam filtering in social networks is increasingly important owing to the rapid growth of social network user base. Sophisticated spam filters must be developed to deal with this complex problem. Traditional machine learning approaches such as neural networks, support vector machine and Naïve Bayes classifiers are not effective enough to process and utilize complex features present in high-dimensional data on social network spam. To overcome this problem, here we propose a novel approach to social network spam filtering. The approach uses ensemble learning techniques with regularized deep neural networks as base learners. We demonstrate that this approach is effective for social network spam filtering on a benchmark dataset in terms of accuracy and area under ROC. In addition, solid performance is achieved in terms of false negative and false positive rates. We also show that the proposed approach outperforms other popular algorithms used in spam filtering, such as decision trees, Naïve Bayes, artificial immune systems, support vector machines, etc.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IFIP Advances in Information and Communication Technology. Vol. 519
ISBN
978-3-319-92006-1
ISSN
1868-4238
e-ISSN
neuvedeno
Number of pages
11
Pages from-to
38-48
Publisher name
Springer
Place of publication
Heidelberg
Event location
Rhodos
Event date
May 25, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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