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A New Dynamic Rule Activation Method for Extended Belief Rule-Based Systems

Calzada, Alberto; Liu, Jun; Wang, Hui; Kashyap, Anil

Authors

Alberto Calzada

Jun Liu

Hui Wang

Dr Anil Kashyap Anil.Kashyap@uwe.ac.uk
Head of Department of Geography & Environment



Abstract

© 2014 IEEE. Data incompleteness and inconsistency are common issues in data-driven decision models. To some extend, they can be considered as two opposite circumstances, since the former occurs due to lack of information and the latter can be regarded as an excess of heterogeneous information. Although these issues often contribute to a decrease in the accuracy of the model, most modeling approaches lack of mechanisms to address them. This research focuses on an advanced belief rule-based decision model and proposes a dynamic rule activation (DRA) method to address both issues simultaneously. DRA is based on "smart" rule activation, where the actived rules are selected in a dynamic way to search for a balance between the incompleteness and inconsistency in the rule-base generated from sample data to achive a better performance. A series of case studies demonstrate how the use of DRA improves the accuracy of this advanced rule-based decision model, without compromising its efficiency, especially when dealing with multi-class classification datasets. DRA has been proved to be beneficial to select the most suitable rules or data instances instead of aggregating an entire rule-base. Beside the work performed in rule-based systems, DRA alone can be regarded as a generic dynamic similarity measurement that can be applied in different domains.

Journal Article Type Article
Publication Date Apr 1, 2015
Journal IEEE Transactions on Knowledge and Data Engineering
Print ISSN 1041-4347
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 27
Issue 4
Pages 880-894
APA6 Citation Calzada, A., Liu, J., Wang, H., & Kashyap, A. (2015). A New Dynamic Rule Activation Method for Extended Belief Rule-Based Systems. IEEE Transactions on Knowledge and Data Engineering, 27(4), 880-894. https://doi.org/10.1109/TKDE.2014.2356460
DOI https://doi.org/10.1109/TKDE.2014.2356460
Keywords pragmatics, data models, vectors, accuracy, heuristic algorithms, uncertainty, knowledge based systems
Publisher URL http://dx.doi.org/10.1109/TKDE.2014.2356460