Digitální knihovnaUPCE
 

Spam filtering in social networks using regularized deep neural networks with ensemble learning

Konferenční objektpeer-reviewedpostprint
Náhled

Datum publikování

2018

Vedoucí práce

Oponent

Název časopisu

Název svazku

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

Trvalý odkaz na tento záznam

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

Studijní obor

Studijní program

Signatura tištěné verze

Umístění tištěné verze

Přístup k tištěné verzi

Klíčová slova

Meta-learning, Neural network, Regularization, Social networks

Endorsement

Review

item.page.supplemented

item.page.referenced