Spam Filtering Using Regularized Neural Networks with Rectified Linear Units
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Datum publikování
2016
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Springer
Abstrakt
The rapid growth of unsolicited and unwanted messages has inspired the development of many anti-spam methods. Machine-learning methods such as Naïve Bayes (NB), support vector machines (SVMs) or neural networks (NNs) have been particularly effective in categorizing spam /non-spam messages. They automatically construct word lists and their weights usually in a bag-of-words fashion. However, traditional multilayer perceptron (MLP) NNs usually suffer from slow optimization convergence to a poor local minimum and overfitting issues. To overcome this problem, we use a regularized NN with rectified linear units (RANN-ReL) for spam filtering. We compare its performance on three benchmark spam datasets (Enron, SpamAssassin, and SMS spam collection) with four machine algorithms commonly used in text classification, namely NB, SVM, MLP, and k-NN. We show that the RANN-ReL outperforms other methods in terms of classification accuracy, false negative and false positive rates. Notably, it classifies well both major (legitimate) and minor (spam) classes.
Rozsah stran
p. 65-75
ISSN
0302-9743
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Projekt
SGS_2016_023/Ekonomický a sociální rozvoj v soukromém a veřejném sektoru
Zdrojový dokument
AIIA 2016 Advances in Artificial Intelligence
Vydavatelská verze
http://link.springer.com/chapter/10.1007/978-3-319-49130-1_6
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Pouze v rámci univerzity
Název akce
15th International Conference of the Italian Association for Artificial Intelligence (28.11.2016 - 01.12.2016)
ISBN
978-3-319-49129-5
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Klíčová slova
Spam filter, Email, Sms, Neural network, Regularization, Rectified linear unit, Spamový filtr, Email, Sms, neuronová síť, regularizace, rektifikovaná lineární jednotka