Evaluating and optimizing autonomous text classification systems
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Multi-agent information classification using dynamic acquaintance lists
Journal of the American Society for Information Science and Technology
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Multiagent systems and information retrieval our experience with X.MAS
Expert Systems with Applications: An International Journal
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Automatic text classification is an important operational problem in digital library practice. Most text classification efforts so far concentrated on developing centralized solutions. However, centralized classification approaches often are limited due to constraints on knowledge and computing resources. In addition, centralized approaches are more vulnerable to attacks or system failures and less robust in dealing with them. We present a de-centralized approach and system implementation (named MACCI) for text classification using a multi-agent framework. Experiments are conducted to compare our multi-agent approach with a centralized approach. The results show multi-agent classification can achieve promising classification results while maintaining its other advantages.