Spam filtering in social networks using regularized deep neural networks with ensemble learning
Konferenční objektOtevřený přístuppeer-reviewedpostprintSoubory
Datum publikování
2018
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Vydavatel
Springer
Abstrakt
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.
Rozsah stran
p. 38-48
ISSN
1868-4238
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Projekt
SGS_2018_019/Pokročilá podpora rozvoje chytrých měst a regionů
Zdrojový dokument
IFIP Advances in Information and Communication Technology. Vol. 519
Vydavatelská verze
https://link.springer.com/chapter/10.1007/978-3-319-92007-8_4
Přístup k e-verzi
open access
Název akce
14th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2018 (25.05.2018 - 27.05.2018, Rhodos)
ISBN
978-3-319-92006-1
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Klíčová slova
Meta-learning, Neural network, Regularization, Social networks