Song, Dawei; Lau, Raymond Y.K.; Bruza, Peter D.; Wong, Kam-Fai and Chen, Ding-Yi
(2007).
| DOI (Digital Object Identifier) Link: | https://doi.org/10.1016/j.dss.2007.04.001 |
|---|---|
| Google Scholar: | Look up in Google Scholar |
Abstract
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. (C) 2007 Elsevier B.V. All rights reserved.
| Item Type: | Journal Item |
|---|---|
| ISSN: | 0167-9236 |
| Keywords: | Information inference; Information flow; Belief revision; Document classification; Information agents |
| Academic Unit/School: | Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications Faculty of Science, Technology, Engineering and Mathematics (STEM) |
| Item ID: | 15675 |
| Depositing User: | Colin Smith |
| Date Deposited: | 14 Apr 2009 14:17 |
| Last Modified: | 29 Nov 2016 16:00 |
| URI: | http://oro.open.ac.uk/id/eprint/15675 |
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