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Combining Prioritized Decisions in Classification

Bi, Yaxin, Wu, Shengli and Guo, Gongde (2007) Combining Prioritized Decisions in Classification. In: MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence. Springer-Verlag Berlin. 11 pp. [Conference contribution]

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Abstract

In this paper we present an alternative evidential method of combining prioritized decisions, in order to arrive at a "consensus", or aggregate, decision. Previous studies have suggested that, in some classification domains, the better performance can be achieved through combining the first and second decisions from each evidence source. However, it is easy to illustrate the fact that going further down a decision list, to give longer preferred decisions, can provide the alternative to the method of combining only the first one and second decisions. Our objective here is to examine the theoretical aspect of an alternative method in terms of <em>quartet</em>﾿ how extending a decision list of any length by one extra preferred decision affects classification results. We also present the experimental results to demonstrate the effectiveness of our alternative method.

Item Type:Conference contribution (Paper)
Keywords:Ensemble Learning, Prioritized Decisions, Classification
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:25501
Deposited By: Dr Yaxin Bi
Deposited On:21 Jan 2016 16:30
Last Modified:21 Jan 2016 16:30

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