Journal ArticleDOI
SMS Spam Detection Using Noncontent Features
TLDR
This service-side solution uses graph data mining to distinguish spammers from nonspammers and detect spam without checking a message's contents.Abstract:
Short Message Service text messages are indispensable, but they face a serious problem from spamming. This service-side solution uses graph data mining to distinguish spammers from nonspammers and detect spam without checking a message's contents.read more
Citations
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Journal Article
Review on Text Classification by NLP Approaches with Machine Learning and Data Mining Approaches
Gurvir Kaur,Parvinder Kaur +1 more
TL;DR: In this paper review on the different techniques of text classification is discussed, and NLP has huge have basic components with the field of computational phonetics, and is regularly viewed as a sub-field of computerized reasoning.
Dissertation
Exploring heterogeneous social information networks for recommendation
TL;DR: Exploring Heterogeneous Social Information Networks for Recommendation shows clear trends in how these networks are modified over time to improve the quality of information and reduce the number of errors.
Journal Article
Antyscam – practical web spam classifier
TL;DR: The web spam recognition system that periodically refreshes the learning set to create an adequate classifier is presented and it is proved that such strategy is better than an incrementation of thelearning set.
Journal ArticleDOI
SMS Phishing Detection Using Oversampling and Feature Optimization Method
TL;DR: Experimental results show oversampling method and feature optimization method improve the accuracy of SMS phishing detection.
References
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LIBLINEAR: A Library for Large Linear Classification
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Machine learning in automated text categorization
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Proceedings ArticleDOI
EigenRank: a ranking-oriented approach to collaborative filtering
Nathan Liu,Qiang Yang +1 more
TL;DR: This paper proposes a collaborative filtering approach that addresses the item ranking problem directly by modeling user preferences derived from the ratings and shows that the proposed approach outperforms traditional collaborative filtering algorithms significantly on the NDCG measure for evaluating ranked results.