Conference proceeding
Automated text classification using a multi-agent framework
Proceedings of the 5th ACM/IEEE-CS joint conference on digital libraries
07 Jun 2005
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
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Details
- Title
- Automated text classification using a multi-agent framework
- Creators
- Yueyu Fu - Indiana University BloomingtonWeimao Ke - Indiana University BloomingtonJaved Mostafa - Indiana University BloomingtonACM
- Publication Details
- Proceedings of the 5th ACM/IEEE-CS joint conference on digital libraries
- Series
- JCDL '05
- Publisher
- ACM
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000230429800027
- Scopus ID
- 2-s2.0-27544492594
- Other Identifier
- 991020546596704721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Information Systems
- Computer Science, Interdisciplinary Applications
- Information Science & Library Science