Combining Prioritized Decisions in Classification

Yaxin Bi, Shengli Wu, Gongde Guo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 quartet﾿ 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.
LanguageEnglish
Title of host publicationUnknown Host Publication
Number of pages11
Publication statusPublished - 2007
EventMDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence -
Duration: 1 Jan 2007 → …

Conference

ConferenceMDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
Period1/01/07 → …

Keywords

  • Ensemble Learning
  • Prioritized Decisions
  • Classification

Cite this

Bi, Y., Wu, S., & Guo, G. (2007). Combining Prioritized Decisions in Classification. In Unknown Host Publication
Bi, Yaxin ; Wu, Shengli ; Guo, Gongde. / Combining Prioritized Decisions in Classification. Unknown Host Publication. 2007.
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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 quartet﾿ 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.",
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Bi, Y, Wu, S & Guo, G 2007, Combining Prioritized Decisions in Classification. in Unknown Host Publication. MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence, 1/01/07.

Combining Prioritized Decisions in Classification. / Bi, Yaxin; Wu, Shengli; Guo, Gongde.

Unknown Host Publication. 2007.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Combining Prioritized Decisions in Classification

AU - Bi, Yaxin

AU - Wu, Shengli

AU - Guo, Gongde

PY - 2007

Y1 - 2007

N2 - 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 quartet﾿ 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.

AB - 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 quartet﾿ 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.

KW - Ensemble Learning

KW - Prioritized Decisions

KW - Classification

M3 - Conference contribution

BT - Unknown Host Publication

ER -

Bi Y, Wu S, Guo G. Combining Prioritized Decisions in Classification. In Unknown Host Publication. 2007