An intelligent information agent for document title classification and filtering in document-intensive domains

Effective decision making is based on accurate and timely information. However, human decision makers are often overwhelmed by the huge amount of electronic data these days. The main contribution of this paper is the development of effective information agents which can autonomously classify and filter incoming electronic data on behalf of their human users. The proposed information agents are innovative because they can quickly classify electronic documents solely based on the short titles of these documents. Moreover, supervised learning is not required to train the classification models of these agents. Document classification is based on information inference conducted over a high dimensional semantic information space. What is more, a belief revision mechanism continuously maintains a set of user preferred information categories and filter documents with respect to these categories. Preliminary experimental results show that our document classification and filtering mechanism outperforms the Support Vector Machines (SVM) model which is regarded as one of the best performing classifiers.