Automated text classification using a multi-agent framework

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 decentralized 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. Categories and Subject Descriptors H.3.4 [Information Storage and Retrieval]: Systems and Software – distributed systems, performance evaluation General Terms Algorithms, Design, Experimentation Keywords Classificat...

• Type: Conference
• Year: 2005
• Where: JCDL
• Authors: Yueyu Fu, Weimao Ke, Javed Mostafa
• Keywords: Centralized, Centralized Classification Approaches, JCDL 2005, Text Classification