SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Data mining: concepts and techniques
Data mining: concepts and techniques
Distributed Manufacturing Scheduling Using Intelligent Agents
IEEE Intelligent Systems
A statistical approach to the spam problem
Linux Journal
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Spam filters: bayes vs. chi-squared; letters vs. words
ISICT '03 Proceedings of the 1st international symposium on Information and communication technologies
An empirical study of spam traffic and the use of DNS black lists
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Essentials of Business Information Systems (7th Edition)
Essentials of Business Information Systems (7th Edition)
Applying lazy learning algorithms to tackle concept drift in spam filtering
Expert Systems with Applications: An International Journal
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A multiagent-based peer-to-peer network in Java for distributed spam filtering
CEEMAS'03 Proceedings of the 3rd Central and Eastern European conference on Multi-agent systems
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Review: A review of machine learning approaches to Spam filtering
Expert Systems with Applications: An International Journal
iDetect: Content Based Monitoring of Complex Networks using Mobile Agents
Applied Soft Computing
An implementation and evaluation of recommender systems for traveling abroad
Expert Systems with Applications: An International Journal
Detecting malicious tweets in trending topics using a statistical analysis of language
Expert Systems with Applications: An International Journal
Categorization of malicious behaviors using ontology-based cognitive agents
Data & Knowledge Engineering
Hi-index | 12.06 |
Spam continues to generate great interest both among academicians and practitioners. Many spam filtering techniques have made considerable progress in recent years. The predominant approaches include data mining methods and machine learning methods. Researchers have largely focused on either one of the approaches since a unified framework is still lacking. To fill the gap in the literature, this paper inherits the credit-assignment problem by proposing a collaborative learning framework that could credit or blame each selected heterogeneous technique. The results of this study indicate that the collaborative learning framework is simple and comprehensible. In addition, we found that the framework offers a principle solution to combine heterogeneous individual technique to collaborative filtering for anti-spam problems.