ACSC '06 Proceedings of the 29th Australasian Computer Science Conference - Volume 48
Exploiting temporal contexts in text classification
Proceedings of the 17th ACM conference on Information and knowledge management
Using Knowledge Base for Event-Driven Scheduling of Web Monitoring Systems
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Temporally-aware algorithms for document classification
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Online knowledge validation with prudence analysis in a document management application
Expert Systems with Applications: An International Journal
Automated information mediator for HTML and XML based web information delivery service
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
User behavior analysis of the open-ended document classification system
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
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With the explosive increase in web basedinformation, the need for an intelligent agent forautomatic classification has also been increasedresulting in many research discoveries in this area.Machine Learning (ML) based document classificationis now the prevalent approach. However, classificationby ML may not keep the same performance because theknowledge generated from the training set may not beappropriate for certain types of web information.People are often concerned more about the newlyuploaded information such as web based online newsthan information already available. This explains whyit is not widely used in real applications. However, themanual classification method, by the domain users,cannot be a solution either until the knowledgeacquisition bottleneck issue is resolved. MultipleClassification Ripple Down Rules, an incrementalknowledge acquisition method, is suggested toovercome this problem with fast learning and low costmaintenance.