Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Evaluating cost-sensitive Unsolicited Bulk Email categorization
Proceedings of the 2002 ACM symposium on Applied computing
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An evaluation of statistical spam filtering techniques
ACM Transactions on Asian Language Information Processing (TALIP)
Combining winnow and orthogonal sparse bigrams for incremental spam filtering
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Content based SMS spam filtering
Proceedings of the 2006 ACM symposium on Document engineering
Feature engineering for mobile (SMS) spam filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Spam filtering for short messages
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Email Spam Filtering: A Systematic Review
Foundations and Trends in Information Retrieval
Feature shaping for linear SVM classifiers
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluation of Approaches for Dimensionality Reduction Applied with Naive Bayes Anti-Spam Filters
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
Filtering spams using the minimum description length principle
Proceedings of the 2010 ACM Symposium on Applied Computing
Detection of near-duplicate user generated contents: the SMS spam collection
Proceedings of the 3rd international workshop on Search and mining user-generated contents
Review: SMS spam filtering: Methods and data
Expert Systems with Applications: An International Journal
$100,000 prize jackpot. call now!: identifying the pertinent features of SMS spam
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Simple SMS spam filtering on independent mobile phone
Security and Communication Networks
A novel probabilistic feature selection method for text classification
Knowledge-Based Systems
Stream-based event prediction using bayesian and bloom filters
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
The curse of 140 characters: evaluating the efficacy of SMS spam detection on android
Proceedings of the Third ACM workshop on Security and privacy in smartphones & mobile devices
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The growth of mobile phone users has lead to a dramatic increasing of SMS spam messages. In practice, fighting mobile phone spam is difficult by several factors, including the lower rate of SMS that has allowed many users and service providers to ignore the issue, and the limited availability of mobile phone spam-filtering software. On the other hand, in academic settings, a major handicap is the scarcity of public SMS spam datasets, that are sorely needed for validation and comparison of different classifiers. Moreover, as SMS messages are fairly short, content-based spam filters may have their performance degraded. In this paper, we offer a new real, public and non-encoded SMS spam collection that is the largest one as far as we know. Moreover, we compare the performance achieved by several established machine learning methods. The results indicate that Support Vector Machine outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison.