Conference proceeding
Collaborative classifier agents: studying the impact of learning in distributed document classification
Proceedings of the 7th ACM/IEEE-CS joint conference on digital libraries, pp 428-437
18 Jun 2007
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We developed a multi-agent framework where agents had limited/distributed knowledge for document classification and collaborated with each other to overcome the knowledge distribution. Each agent was equipped with a certain learning algorithm for predicting potential collaborators, or helping agents. We conducted experimental research on a standard news corpus to examine the impact of two learning algorithms: Pursuit Learning and Nearest Centroid Learning. For a fundamental retrieval operation, namely classification, both algorithms achieved competitive classification effectiveness and efficiency. Subsequently, the impact of the learning exploration rate and the maximum collaboration range on classification effectiveness and efficiency were examined. Close investigation of agent learning dynamics revealed increasing and stabilizing patterns that were enhanced by the learning algorithms.
Metrics
Details
- Title
- Collaborative classifier agents
- Creators
- Weimao KeJaved MostafaYueyu Fu
- Publication Details
- Proceedings of the 7th ACM/IEEE-CS joint conference on digital libraries, pp 428-437
- Conference
- 7th ACM/IEEE-CS joint conference on digital libraries, 7th
- Series
- JCDL '07
- Publisher
- Association for Computing Machinery (ACM)
- Number of pages
- 1
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000266062800068
- Scopus ID
- 2-s2.0-36348959153
- Other Identifier
- 991014877683904721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Information Systems
- Engineering, Electrical & Electronic
- Information Science & Library Science